Everything You Need To Become A Machine Learner

This list of resources is specifically targeted at Web Developers and Data Scientists.… so do with it what you will…

This list borrows heavily from multiple lists created by : sindresorhus

Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. Artificial intelligence systems are used to perform complex tasks in a way that is similar to how humans solve problems.

The goal of AI is to create computer models that exhibit "intelligent behaviors" like humans, according to Boris Katz, a principal research scientist and head of the InfoLab Group at CSAIL. This means machines that can recognize a visual scene, understand a text written in natural language, or perform an action in the physical world.

Machine learning is one way to use AI. It was defined in the 1950s by AI pioneer Arthur Samuel as "the field of study that gives computers the ability to learn without explicitly being programmed."

• [📖] Delivering Happiness

• [📖] Good to Great: Why Some Companies Make the Leap...And Others Don't

• [📖] Hello, Startup: A Programmer's Guide to Building Products, Technologies, and Teams

• [📖] How Google Works

• [📖] Learn to Earn: A Beginner's Guide to the Basics of Investing and Business

• [📖] Rework

• [📖] The Airbnb Story

• [📖] The Personal MBA

• [ ] Facebook: Digital marketing: get started

• [ ] Facebook: Digital marketing: go further

• [ ] Google Analytics for Beginners

• [ ] Moz: The Beginner's Guide to SEO

• [ ] Smartly: Marketing Fundamentals

• [ ] Treehouse: SEO Basics

• [🅤]ꭏ App Monetization

• [🅤]ꭏ App Marketing

• [🅤]ꭏ How to Build a Startup

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Natural language processing

Natural language processing is a field of machine learning in which machines learn to understand natural language as spoken and written by humans, instead of the data and numbers normally used to program computers. This allows machines to recognize language, understand it, and respond to it, as well as create new text and translate between languages. Natural language processing enables familiar technology like chatbots and digital assistants like Siri or Alexa.

Neural networks

Neural networks are a commonly used, specific class of machine learning algorithms. Artificial neural networks are modeled on the human brain, in which thousands or millions of processing nodes are interconnected and organized into layers.

In an artificial neural network, cells, or nodes, are connected, with each cell processing inputs and producing an output that is sent to other neurons. Labeled data moves through the nodes, or cells, with each cell performing a different function. In a neural network trained to identify whether a picture contains a cat or not, the different nodes would assess the information and arrive at an output that indicates whether a picture features a cat.

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Be familiar with howMachine Learning is applied at other companies

• [📰] How Facebook uses super-efficient AI models to detect hate speech

• [📰] Recent Advances in Google Translate

• [📰] Cannes: HowMachine Learning saves us $1.7M a year on document previews

• [📰] Machine Learning @ Monzo in 2020

• [📰] How image search works at Dropbox

• [ ] Real-world AI Case Studies

  • [ ] Andrej Karpathy on AI at Tesla (Full Stack Deep Learning - August 2018)
  • [ ] Jai Ranganathan at Data Science at Uber (Full Stack Deep Learning - August 2018)
  • [ ] John Apostolopoulos of Cisco discusses "Machine Learning in Networking" 0:48:44
  • [ ] Joaquin Candela, Director of Applied Machine Learning , Facebook in conversation with Esteban Arcaute 0:52:27
  • [ ] Eric Colson, Chief Algorithms Officer, Stitch Fix 0:53:57
  • [ ] Claudia Perlich, Advisor to Dstillery and Adjunct Professor NYU Stern School of Business 0:51:59
  • [ ] Jeff Dean, Google Senior Fellow and SVP Google AI - Deep Learning to Solve Challenging Problems 0:58:45
  • [ ] James Parr, Director of Frontier Development Lab (NASA), FDL Europe & CEO, Trillium Technologies 0:55:46
  • [ ] Daphne Koller, Founder & CEO of Insitro - In Conversation with Carlos Bustamante 0:49:29
  • [ ] Eric Horvitz, Microsoft Research - AI in the Open World: Advances, Aspirations, and Rough Edges 0:56:11
  • [ ] Tony Jebara, Netflix - Machine Learning for Recommendation and Personalization 0:55:20

• [💻] Analyzing Police Activity with pandas

• [💻] HR Analytics in Python: Predicting Employee Churn

• [💻] Predicting Customer Churn in Python

• [📺 ] How does YouTube recommend videos? - AI EXPLAINED! 0:33:53

• [📺 ] How does Google Translate's AI work? 0:15:02

• [📺 ] Data Science in Finance 0:17:52

• [📺 ] The Age of AI

  • [ ] How Far is Too Far? | The Age of A.I. 0:34:39
  • [ ] Healed through A.I. | The Age of A.I. 0:39:55
  • [ ] Using A.I. to build a better human | The Age of A.I. 0:44:27
  • [ ] Love, art and stories: decoded | The Age of A.I. 0:38:57
  • [ ] The 'Space Architects' of Mars | The Age of A.I. 0:30:10
  • [ ] Will a robot take my job? | The Age of A.I. 0:36:14
  • [ ] Saving the world one algorithm at a time | The Age of A.I. 0:46:37
  • [ ] How A.I. is searching for Aliens | The Age of A.I. 0:36:12

• [📺 ] Using Intent Data to Optimize the Self-Solve Experience

• [📺 ] Trillions of Questions, No Easy Answers: A (home) movie about how Google Search works

• [📺 ] Google Machine Learning System Design Mock Interview

• [📺 ] Netflix Machine Learning Mock Interview: Type-ahead Search

• [📺 ] Machine Learning design: Search engine for Q&A

• [📺 ] Engineering Systems for Real-Time Predictions @DoorDash

• [📺 ] How Gmail Uses Iterative Design, Machine Learning and AI to Create More Assistive Features

• [📺 ] Wayfair Data Science Explains It All: Human-in-the-loop Systems

• [📺 ] Leaving the lab: Building NLP applications that real people can use

• [📺 ] Machine Learning at Uber (Natural Language Processing Use Cases)

• [📺 ] Google Wave: Natural Language Processing

• [📺 ] Natural Language Understanding in Alexa

• [📺 ] The Machine Learning Behind Alexa’s AI Systems

• [📺 ] Ines Montani Keynote - Applied NLP Thinking

• [📺 ] Lecture 9: Lukas Biewald

• [📺 ] Lecture 13: Research Directions

• [📺 ] Lecture 14: Jeremy Howard

• [📺 ] Lecture 15: Richard Socher

• [📺 ] Machine learning across industries with Vicki Boykis 0:34:02

• [📺 ] Rachael Tatman - Conversational A.I. and Linguistics 0:36:51

• [📺 ] Nicolas Koumchatzky - Machine Learning in Production for Self Driving Cars 0:44:56

• [📺 ] Brandon Rohrer - Machine Learning in Production for Robots 0:34:31

• [📺 ] [CVPR'21 WAD] Keynote - Andrej Karpathy, Tesla

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Be able to frame anMachine Learning problem

• [ ] AWS: Types of Machine Learning Solutions

• [📰] Apply Machine Learning to your Business

• [📰] Resilience and Vibrancy: The 2020 Data & AI Landscape

• [📰] Software 2.0

• [📰] Highlights from ICML 2020

• [📰] A Peek at Trends in Machine Learning

• [📰] How to deliver on Machine Learning projects

• [📰] Data Science as a Product

• [📰] Customer service is full of machine learning problems

• [📰] Choosing Problems in Data Science and Machine Learning

• [📰] Why finance is deploying natural language processing

• [📰] The Last 5 Years In Deep Learning

• [📰] Always start with a stupid model, no exceptions.

• [📰] Most impactful AI trends of 2018: the rise ofMachine Learning Engineering

• [📰] Building machine learning products: a problem well-defined is a problem half-solved.

• [📰] Simple considerations for simple people building fancy neural networks

• [📰] Maximizing Business Impact with Machine Learning

• [📖] AI Superpowers: China, Silicon Valley, and the New World Order

• [📖] A Human's Guide to Machine Intelligence

• [📖] The Future Computed

• [📖] Machine Learning Yearning by Andrew Ng

• [📖] Prediction Machines: The Simple Economics of Artificial Intelligence

• [📖] Building Machine Learning Powered Applications: Going from Idea to Product

• [ ] Coursera: AI For Everyone

• [💻] Data Science for Everyone

• [💻] Machine Learning with the Experts: School Budgets

• [💻] Machine Learning for Everyone

• [💻] Data Science for Managers

• [ ] Facebook: Field Guide to Machine Learning

• [G] Introduction to Machine Learning Problem Framing

• [ ] Pluralsight: How to Think About Machine Learning Algorithms

• [ ] State of AI Report 2020

• [📺 ] Vincent Warmerdam: The profession of solving (the wrong problem) | PyData Amsterdam 2019

• [📺 ] Hugging Face, Transformers | NLP Research and Open Source | Interview with Julien Chaumond

• [📺 ] Vincent Warmerdam - Playing by the Rules-Based-Systems | PyData Eindhoven 2020

• [📺 ] Building intuitions before building models

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Be familiar with data ethics

• [📰] How to Detect Bias in AI

• [ ] Netflix: Coded Bias

• [ ] Netflix: The Great Hack

• [ ] Netflix: The Social Dilemma

• [ ] Practical Data Ethics

  • [ ] Lesson 1: Disinformation
  • [ ] Lesson 2: Bias & Fairness
  • [ ] Lesson 3: Ethical Foundations & Practical Tools
  • [ ] Lesson 4: Privacy and surveillance
  • [ ] Lesson 4 continued: Privacy and surveillance
  • [ ] Lesson 5.1: The problem with metrics
  • [ ] Lesson 5.2: Our Ecosystem, Venture Capital, & Hypergrowth
  • [ ] Lesson 5.3: Losing the Forest for the Trees, guest lecture by Ali Alkhatib
  • [ ] Lesson 6: Algorithmic Colonialism, and Next Steps

• [📺 ] Lecture 9: Ethics (Full Stack Deep Learning - Spring 2021) 1:04:50

• [📺 ] SE4AI: Ethics and Fairness 1:18:37

• [📺 ] SE4AI: Security 1:18:24

• [📺 ] SE4AI: Safety 1:17:37

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Be able to import data from multiple sources

• [ ] Docs: Beautiful Soup Documentation

• [💻] Importing Data in Python (Part 2)

• [💻] Web Scraping in Python

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Be able to setup data annotation efficiently

• [📰] Create A Synthetic Image Dataset — The “What”, The “Why” and The “How”

• [📰] We need Synthetic Data

• [📰] Weak Supervision for Online Discussions

• [📰] Machine Learning Infrastructure Tools for Data Preparation

• [📰] Exploring the Role of Human Raters in Creating NLP Datasets

• [📰] Inter-Annotator Agreement (IAA)

• [📰] How to compute inter-rater reliability metrics (Cohen’s Kappa, Fleiss’s Kappa, Cronbach Alpha, Krippendorff Alpha, Scott’s Pi, Inter-class correlation) in Python

• [📺 ] Snorkel: Dark Data and Machine Learning - Christopher Ré

• [📺 ] Training a NER Model with Prodigy and Transfer Learning

• [📺 ] Training a New Entity Type with Prodigy – annotation powered by active learning

• [📺 ] ECCV 2020 WSL tutorial: 4. Human-in-the-loop annotations

• [📺 ] Active Learning: Why Smart Labeling is the Future of Data Annotation | Alectio

• [📺 ] Lecture 8: Data Management (Full Stack Deep Learning - Spring 2021) 0:59:42

• [📺 ] Lab 6: Data Labeling (Full Stack Deep Learning - Spring 2021) 0:05:06

• [📺 ] Lecture 6: Data Management

• [📺 ] SE4AI: Data Quality 1:07:15

• [📺 ] SE4AI: Data Programming and Intro to Big Data Processing 0:33:04

• [📺 ] SE4AI: Managing and Processing Large Datasets 1:21:27

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Be able to manipulate data with Numpy

• [📰] A Visual Intro to NumPy and Data Representation

• [📰] Good practices with numpy random number generators

• [📰] NumPy Illustrated: The Visual Guide to NumPy

• [📰] NumPy Fundamentals for Data Science and Machine Learning

• [💻] Intro to Python for Data Science

• [ ] Pluralsight: Working with Multidimensional Data Using NumPy

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Be able to manipulate data with Pandas

• [📰] Visualizing Pandas' Pivoting and Reshaping Functions

• [📰] A Gentle Visual Intro to Data Analysis in Python Using Pandas

• [📰] Comprehensive Guide to Grouping and Aggregating with Pandas

• [📰] 8 Python Pandas Value_counts() tricks that make your work more efficient

• [💻] pandas Foundations

• [💻] Pandas Joins for Spreadsheet Users

• [💻] Manipulating DataFrames with pandas

• [💻] Merging DataFrames with pandas

• [💻] Data Manipulation with pandas

• [💻] Optimizing Python Code with pandas

• [💻] Streamlined Data Ingestion with pandas

• [💻] Analyzing Marketing Campaigns with pandas

• [ ] edX: Implementing Predictive Analytics with Spark in Azure HDInsight

• [📰] Modern Pandas

  • [ ] Modern Pandas (Part 1)
  • [ ] Modern Pandas (Part 2)
  • [ ] Modern Pandas (Part 3)
  • [ ] Modern Pandas (Part 4)
  • [ ] Modern Pandas (Part 5)
  • [ ] Modern Pandas (Part 6)
  • [ ] Modern Pandas (Part 7)
  • [ ] Modern Pandas (Part 8)

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Be able to manipulate data in spreadsheets

• [💻] Spreadsheet basics

• [💻] Data Analysis with Spreadsheets

• [💻] Intermediate Spreadsheets for Data Science

• [💻] Pivot Tables with Spreadsheets

• [💻] Data Visualization in Spreadsheets

• [💻] Introduction to Statistics in Spreadsheets

• [💻] Conditional Formatting in Spreadsheets

• [💻] Marketing Analytics in Spreadsheets

• [💻] Error and Uncertainty in Spreadsheets

• [ ] edX: Analyzing and Visualizing Data with Excel

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Be able to manipulate data in databases

• [ ] Codecademy: SQL Track

• [💻] Intro to SQL for Data Science

• [💻] Introduction to MongoDB in Python

• [💻] Intermediate SQL

• [💻] Exploratory Data Analysis in SQL

• [💻] Joining Data in PostgreSQL

• [💻] Querying with TransactSQL

• [💻] Introduction to Databases in Python

• [💻] Reporting in SQL

• [💻] Applying SQL to Real-World Problems

• [💻] Analyzing Business Data in SQL

• [💻] Data-Driven Decision Making in SQL

• [💻] Database Design

• [🅤]ꭏ SQL for Data Analysis

• [🅤]ꭏ Intro to relational database

• [🅤]ꭏ Database Systems Concepts & Design

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Be able to use Linux

• [📰] Streamline your projects using Makefile

• [📰] Understand Linux Load Averages and Monitor Performance of Linux

• [📰] Command-line Tools can be 235x Faster than your Hadoop Cluster

• [ ] Calmcode: makefiles

• [ ] Calmcode: entr

• [ ] Codecademy: Learn the Command Line

• [💻] Introduction to Shell for Data Science

• [💻] Introduction to Bash Scripting

• [💻] Data Processing in Shell

• [ ] MIT: The Missing Semester of CS Education

  • [ ] Lecture 1: Course Overview + The Shell (2020) 0:48:16
  • [ ] Lecture 2: Shell Tools and Scripting (2020) 0:48:55
  • [ ] Lecture 3: Editors (vim) (2020) 0:48:26
  • [ ] Lecture 4: Data Wrangling (2020) 0:50:03
  • [ ] Lecture 5: Command-line Environment (2020) 0:56:06
  • [ ] Lecture 6: Version Control (git) (2020) 1:24:59
  • [ ] Lecture 7: Debugging and Profiling (2020) 0:54:13
  • [ ] Lecture 8: Metaprogramming (2020) 0:49:52
  • [ ] Lecture 9: Security and Cryptography (2020) 1:00:59
  • [ ] Lecture 10: Potpourri (2020) 0:57:54
  • [ ] Lecture 11: Q&A (2020) 0:53:52

• [ ] Thoughtbot: Mastering the Shell

• [ ] Thoughtbot: tmux

• [🅤]ꭏ Linux Command Line Basics

• [🅤]ꭏ Shell Workshop

• [🅤]ꭏ Configuring Linux Web Servers

• [ ] Web Bos: Command Line Power User

• [📺 ] GNU Parallel

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Be able to perform feature selection and engineering

• [📰] Tips for Advanced Feature Engineering

• [📰] Preparing data for a machine learning model

• [📰] Feature selection for a machine learning model

• [📰] Learning from imbalanced data

• [📰] Hacker's Guide to Data Preparation for Machine Learning

• [📰] Practical Guide to Handling Imbalanced Datasets

• [💻] Analyzing Social Media Data in Python

• [💻] Dimensionality Reduction in Python

• [💻] Preprocessing for Machine Learning in Python

• [💻] Data Types for Data Science

• [💻] Cleaning Data in Python

• [💻] Feature Engineering for Machine Learning in Python

• [💻] Importing & Managing Financial Data in Python

• [💻] Manipulating Time Series Data in Python

• [💻] Working with Geospatial Data in Python

• [💻] Analyzing IoT Data in Python

• [💻] Dealing with Missing Data in Python

• [💻] Exploratory Data Analysis in Python

• [ ] edX: Data Science Essentials

• [🅤]ꭏ Creating an Analytical Dataset

• [📺 ] AppliedMachine Learning 2020 - 04 - Preprocessing 1:07:40

• [📺 ] AppliedMachine Learning 2020 - 11 - Model Inspection and Feature Selection 1:15:15

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Be able to experiment in a notebook

• [📰] Securely storing configuration credentials in a Jupyter Notebook

• [📰] Automatically Reload Modules with %autoreload

• [ ] Calmcode: ipywidgets

• [ ] Documentation: Jupyter Lab

• [ ] Pluralsight: Getting Started with Jupyter Notebook and Python

• [📺 ] William Horton - A Brief History of Jupyter Notebooks

• [📺 ] I Like Notebooks

• [📺 ] I don't like notebooks.- Joel Grus (Allen Institute for Artificial Intelligence)

• [📺 ] Ryan Herr - After model.fit, before you deploy| JupyterCon 2020

• [📺 ] nbdev live coding with Hamel Husain

• [📺 ] How to Use JupyterLab

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Be able to visualize data

• [📰] Creating a Catchier Word Cloud Presentation

• [📰] Effectively Using Matplotlib

• [💻] Introduction to Data Visualization with Python

• [💻] Introduction to Seaborn

• [💻] Introduction to Matplotlib

• [💻] Intermediate Data Visualization with Seaborn

• [💻] Visualizing Time Series Data in Python

• [💻] Improving Your Data Visualizations in Python

• [💻] Visualizing Geospatial Data in Python

• [💻] Interactive Data Visualization with Bokeh

• [📺 ] AppliedMachine Learning 2020 - 02 Visualization and matplotlib 1:07:30

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Be able to do literature review using research papers

• [ ] Paper: A Neural Probabilistic Language Model

• [ ] Paper: Efficient Estimation of Word Representations in Vector Space

• [ ] Paper: Sequence to Sequence Learning with Neural Networks

• [ ] Paper: Neural Machine Translation by Jointly Learning to Align and Translate

• [ ] Paper: Attention Is All You Need

• [ ] Paper: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

• [ ] Paper: XLNet: Generalized Autoregressive Pretraining for Language Understanding

• [ ] Paper: Synonyms Based Term Weighting Scheme: An Extension to TF.IDF

• [ ] Paper: RoBERTa: A Robustly Optimized BERT Pretraining Approach

• [ ] Paper: GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

• [ ] Paper: Amazon.com Recommendations Item-to-Item Collaborative Filtering

• [ ] Paper: Collaborative Filtering for Implicit Feedback Datasets

• [ ] Paper: BPR: Bayesian Personalized Ranking from Implicit Feedback

• [ ] Paper: Factorization Machines

• [ ] Paper: Wide & Deep Learning for Recommender Systems

• [ ] Paper: Neural Factorization Machines for Sparse Predictive Analytics

• [ ] Paper: Multiword Expressions: A Pain in the Neck for NLP

• [ ] Paper: PyTorch: An Imperative Style, High-Performance Deep Learning Library

• [ ] Paper: ALBERT: A LITE BERT FOR SELF-SUPERVISED LEARNING OF LANGUAGE REPRESENTATIONS

• [ ] Paper: Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey

• [ ] Paper: A Simple Framework for Contrastive Learning of Visual Representations

• [ ] Paper: Self-Supervised Learning of Pretext-Invariant Representations

• [ ] Paper: FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence

• [ ] Paper: Self-Labelling via Simultalaneous Clustering and Representation Learning

• [ ] Paper: A survey on Semi-, Self- and Unsupervised Techniques in Image Classification

• [ ] Paper: Multi-document Summarization by using TextRank and Maximal Marginal Relevance for Text in Bahasa Indonesia

• [ ] Paper: Train Once, Test Anywhere: Zero-Shot Learning for Text Classification

• [ ] Paper: Zero-shot Text Classification With Generative Language Models

• [ ] Paper: How to Fine-Tune BERT for Text Classification?

• [ ] Paper: Universal Sentence Encoder

• [ ] Paper: Enriching Word Vectors with Subword Information

• [ ] Paper: Beyond Accuracy: Behavioral Testing of NLP models with CheckList

• [ ] Paper: Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks

• [ ] Paper: Temporal Ensembling for Semi-Supervised Learning

• [ ] Paper: Boosting Self-Supervised Learning via Knowledge Transfer

• [ ] Paper: Follow-up Question Generation

• [ ] Paper: The Hardware Lottery

• [ ] Paper: Question Generation via Overgenerating Transformations and Ranking

• [ ] Paper: Good Question! Statistical Ranking for Question Generation

• [ ] Paper: TowardsMachine Learning Engineering: A Brief History Of TensorFlow Extended (TFX)

• [ ] Paper: Neural Text Generation: A Practical Guide

• [ ] Paper: Pest Management In Cotton Farms: An AI-System Case Study from the Global South

• [ ] Paper: BERT2DNN: BERT Distillation with Massive Unlabeled Data for Online E-Commerce Search

• [ ] Paper: On the surprising similarities between supervised and self-supervised models

• [ ] Paper: All-but-the-Top: Simple and Effective Postprocessing for Word Representations

• [ ] Paper: Simple and Effective Dimensionality Reduction for Word Embeddings

• [ ] Paper: AutoCompete: A Framework for Machine Learning Competitions

• [ ] Paper: Cost-effective Deployment of BERT Models in Serverless Environment

• [ ] Paper: Evaluating Large Language Models Trained on Code

• [📺 ] mixup: Beyond Empirical Risk Minimization (Paper Explained)

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Be able to model problems mathematically

• [ ] 3Blue1Brown: Essence of Calculus

  • [ ] The Essence of Calculus, Chapter 1 0:17:04
  • [ ] The paradox of the derivative | Essence of calculus, chapter 2 0:17:57
  • [ ] Derivative formulas through geometry | Essence of calculus, chapter 3 0:18:43
  • [ ] Visualizing the chain rule and product rule | Essence of calculus, chapter 4 0:16:52
  • [ ] What's so special about Euler's number e? | Essence of calculus, chapter 5 0:13:50
  • [ ] Implicit differentiation, what's going on here? | Essence of calculus, chapter 6 0:15:33
  • [ ] Limits, L'Hôpital's rule, and epsilon delta definitions | Essence of calculus, chapter 7 0:18:26
  • [ ] Integration and the fundamental theorem of calculus | Essence of calculus, chapter 8 0:20:46
  • [ ] What does area have to do with slope? | Essence of calculus, chapter 9 0:12:39
  • [ ] Higher order derivatives | Essence of calculus, chapter 10 0:05:38
  • [ ] Taylor series | Essence of calculus, chapter 11 0:22:19
  • [ ] What they won't teach you in calculus 0:16:22

• [ ] 3Blue1Brown: Essence of linear algebra

  • [ ] Vectors, what even are they? | Essence of linear algebra, chapter 1 0:09:52
  • [ ] Linear combinations, span, and basis vectors | Essence of linear algebra, chapter 2 0:09:59
  • [ ] Linear transformations and matrices | Essence of linear algebra, chapter 3 0:10:58
  • [ ] Matrix multiplication as composition | Essence of linear algebra, chapter 4 0:10:03
  • [ ] Three-dimensional linear transformations | Essence of linear algebra, chapter 5 0:04:46
  • [ ] The determinant | Essence of linear algebra, chapter 6 0:10:03
  • [ ] Inverse matrices, column space and null space | Essence of linear algebra, chapter 7 0:12:08
  • [ ] Nonsquare matrices as transformations between dimensions | Essence of linear algebra, chapter 8 0:04:27
  • [ ] Dot products and duality | Essence of linear algebra, chapter 9 0:14:11
  • [ ] Cross products | Essence of linear algebra, Chapter 10 0:08:53
  • [ ] Cross products in the light of linear transformations | Essence of linear algebra chapter 11 0:13:10
  • [ ] Cramer's rule, explained geometrically | Essence of linear algebra, chapter 12 0:12:12
  • [ ] Change of basis | Essence of linear algebra, chapter 13 0:12:50
  • [ ] Eigenvectors and eigenvalues | Essence of linear algebra, chapter 14 0:17:15
  • [ ] Abstract vector spaces | Essence of linear algebra, chapter 15 0:16:46

• [ ] 3Blue1Brown: Neural networks

  • [ ] But what is a Neural Network? | Deep learning, chapter 1 0:19:13
  • [ ] Gradient descent, how neural networks learn | Deep learning, chapter 2 0:21:01
  • [ ] What is backpropagation really doing? | Deep learning, chapter 3 0:13:54
  • [ ] Backpropagation calculus | Deep learning, chapter 4 0:10:17

• [📰] A Visual Tour of Backpropagation

• [📰] Entropy, Cross Entropy, and KL Divergence

• [📰] Interview Guide to Probability Distributions

• [📰] Introduction to Linear Algebra for Applied Machine Learning with Python

• [📰] Entropy of a probability distribution — in layman’s terms

• [📰] KL Divergence — in layman’s terms

• [📰] Probability Distributions

• [📰] Relearning Matrices as Linear Functions

• [📰] You Could Have Come Up With Eigenvectors - Here's How

• [📰] PageRank - How Eigenvectors Power the Algorithm Behind Google Search

• [📰] Interactive Visualization of Why Eigenvectors Matter

• [📰] Cross-Entropy and KL Divergence

• [📰] Why Randomness Is Information?

• [📰] Basic Probability Theory

• [📰] Math You Need to Succeed InMachine Learning Interviews

• [📖] Basics of Linear Algebra for Machine Learning

• [💻] Introduction to Statistics in Python

• [💻] Foundations of Probability in Python

• [💻] Statistical Thinking in Python (Part 1)

• [💻] Statistical Thinking in Python (Part 2)

• [💻] Statistical Simulation in Python

• [ ] edX: Essential Statistics for Data Analysis using Excel

• [ ] Computational Linear Algebra for Coders

• [ ] Khan Academy: Precalculus

• [ ] Khan Academy: Probability

• [ ] Khan Academy: Differential Calculus

• [ ] Khan Academy: Multivariable Calculus

• [ ] Khan Academy: Linear Algebra

• [ ] MIT: 18.06 Linear Algebra (Professor Strang)

  • [ ] 1. The Geometry of Linear Equations 0:39:49
  • [ ] 2. Elimination with Matrices. 0:47:41
  • [ ] 3. Multiplication and Inverse Matrices 0:46:48
  • [ ] 4. Factorization into A = LU 0:48:05
  • [ ] 5. Transposes, Permutations, Spaces R^n 0:47:41
  • [ ] 6. Column Space and Nullspace 0:46:01
  • [ ] 9. Independence, Basis, and Dimension 0:50:14
  • [ ] 10. The Four Fundamental Subspaces 0:49:20
  • [ ] 11. Matrix Spaces; Rank 1; Small World Graphs 0:45:55
  • [ ] 14. Orthogonal Vectors and Subspaces 0:49:47
  • [ ] 15. Projections onto Subspaces 0:48:51
  • [ ] 16. Projection Matrices and Least Squares 0:48:05
  • [ ] 17. Orthogonal Matrices and Gram-Schmidt 0:49:09
  • [ ] 21. Eigenvalues and Eigenvectors 0:51:22
  • [ ] 22. Diagonalization and Powers of A 0:51:50
  • [ ] 24. Markov Matrices; Fourier Series 0:51:11
  • [ ] 25. Symmetric Matrices and Positive Definiteness 0:43:52
  • [ ] 27. Positive Definite Matrices and Minima 0:50:40
  • [ ] 29. Singular Value Decomposition 0:40:28
  • [ ] 30. Linear Transformations and Their Matrices 0:49:27
  • [ ] 31. Change of Basis; Image Compression 0:50:13
  • [ ] 33. Left and Right Inverses; Pseudoinverse 0:41:52

• [ ] StatQuest: Statistics Fundamentals

  • [ ] StatQuest: Histograms, Clearly Explained 0:03:42
  • [ ] StatQuest: What is a statistical distribution? 0:05:14
  • [ ] StatQuest: The Normal Distribution, Clearly Explained!!! 0:05:12
  • [ ] Statistics Fundamentals: Population Parameters 0:14:31
  • [ ] Statistics Fundamentals: The Mean, Variance and Standard Deviation 0:14:22
  • [ ] StatQuest: What is a statistical model? 0:03:45
  • [ ] StatQuest: Sampling A Distribution 0:03:48
  • [ ] Hypothesis Testing and The Null Hypothesis 0:14:40
  • [ ] Alternative Hypotheses: Main Ideas!!! 0:09:49
  • [ ] p-values: What they are and how to interpret them 0:11:22
  • [ ] How to calculate p-values 0:25:15
  • [ ] p-hacking: What it is and how to avoid it! 0:13:44
  • [ ] Statistical Power, Clearly Explained!!! 0:08:19
  • [ ] Power Analysis, Clearly Explained!!! 0:16:44
  • [ ] Covariance and Correlation Part 1: Covariance 0:22:23
  • [ ] Covariance and Correlation Part 2: Pearson's Correlation 0:19:13
  • [ ] StatQuest: R-squared explained 0:11:01
  • [ ] The Central Limit Theorem 0:07:35
  • [ ] StatQuickie: Standard Deviation vs Standard Error 0:02:52
  • [ ] StatQuest: The standard error 0:11:43
  • [ ] StatQuest: Technical and Biological Replicates 0:05:27
  • [ ] StatQuest - Sample Size and Effective Sample Size, Clearly Explained 0:06:32
  • [ ] Bar Charts Are Better than Pie Charts 0:01:45
  • [ ] StatQuest: Boxplots, Clearly Explained 0:02:33
  • [ ] StatQuest: Logs (logarithms), clearly explained 0:15:37
  • [ ] StatQuest: Confidence Intervals 0:06:41
  • [ ] StatQuickie: Thresholds for Significance 0:06:40
  • [ ] StatQuickie: Which t test to use 0:05:10
  • [ ] StatQuest: One or Two Tailed P-Values 0:07:05
  • [ ] The Binomial Distribution and Test, Clearly Explained!!! 0:15:46
  • [ ] StatQuest: Quantiles and Percentiles, Clearly Explained!!! 0:06:30
  • [ ] StatQuest: Quantile-Quantile Plots (QQ plots), Clearly Explained 0:06:55
  • [ ] StatQuest: Quantile Normalization 0:04:51
  • [ ] StatQuest: Probability vs Likelihood 0:05:01
  • [ ] StatQuest: Maximum Likelihood, clearly explained!!! 0:06:12
  • [ ] Maximum Likelihood for the Exponential Distribution, Clearly Explained! V2.0 0:09:39
  • [ ] Why Dividing By N Underestimates the Variance 0:17:14
  • [ ] Maximum Likelihood for the Binomial Distribution, Clearly Explained!!! 0:11:24
  • [ ] Maximum Likelihood For the Normal Distribution, step-by-step! 0:19:50
  • [ ] StatQuest: Odds and Log(Odds), Clearly Explained!!! 0:11:30
  • [ ] StatQuest: Odds Ratios and Log(Odds Ratios), Clearly Explained!!! 0:16:20
  • [ ] Live 2020-04-20!!! Expected Values 0:33:00

• [🅤]ꭏ Eigenvectors and Eigenvalues

• [🅤]ꭏ Linear Algebra Refresher

• [🅤]ꭏ Statistics

• [🅤]ꭏ Intro to Descriptive Statistics

• [🅤]ꭏ Intro to Inferential Statistics

---

Be able to setup project structure

• [📰] pydantic

• [📰] Organizing machine learning projects: project management guidelines

• [📰] Logging and Debugging in Machine Learning - How to use Python debugger and the logging module to find errors in your AI application

• [📰] Best practices to write Deep Learning code: Project structure, OOP, Type checking and documentation

• [📰] Configuring Google Colab Like A Pro

• [📰] Stop using print, start using loguru in Python

• [📰] Hypermodern Python

• [📰] Hypermodern Python Chapter 2: Testing

• [📰] Hypermodern Python Chapter 3: Linting

• [📰] Hypermodern Python Chapter 4: Typing

• [📰] Hypermodern Python Chapter 5: Documentation

• [📰] Hypermodern Python Chapter 6: CI/CD

• [📰] Push and pull: when and why to update your dependencies

• [📰] Reproducible and upgradable Conda environments: dependency management with conda-lock

• [📰] Options for packaging your Python code: Wheels, Conda, Docker, and more

• [📰] Making model training scripts robust to spot interruptions

• [ ] Calmcode: logging

• [ ] Calmcode: tqdm

• [ ] Calmcode: virtualenv

• [ ] Coursera: Structuring Machine Learning Projects

• [ ] Doc: Python Lifecycle Training

• [💻] Introduction to Data Engineering

• [💻] Conda Essentials

• [💻] Conda for Building & Distributing Packages

• [💻] Software Engineering for Data Scientists in Python

• [💻] Designing Machine Learning Workflows in Python

• [💻] Object-Oriented Programming in Python

• [💻] Command Line Automation in Python

• [💻] Creating Robust Python Workflows

• [ ] Developing Python Packages

• [ ] Treehouse: Object Oriented Python

• [ ] Treehouse: Setup Local Python Environment

• [🅤]ꭏ Writing READMEs

• [📺 ] Lecture 1: Introduction to Deep Learning

• [📺 ] Lecture 2: Setting Up Machine Learning Projects

• [📺 ] Lecture 3: Introduction to the Text Recognizer Project

• [📺 ] Lecture 4: Infrastructure and Tooling

• [📺 ] Hydra configuration

• [📺 ] Continuous integration

• [📺 ] Data Engineering +Machine Learning + Software Engineering // Satish Chandra Gupta // MLOps Coffee Sessions #16

• [📺 ] OO Design and Testing Patterns for Machine Learning with Chris Gerpheide

• [📺 ] Tutorial: Sebastian Witowski - Modern Python Developer's Toolkit

• [📺 ] Lecture 13:Machine Learning Teams (Full Stack Deep Learning - Spring 2021) 0:58:13

• [📺 ] Lecture 5:Machine Learning Projects (Full Stack Deep Learning - Spring 2021) 1:13:14

• [📺 ] Lecture 6: Infrastructure & Tooling (Full Stack Deep Learning - Spring 2021) 1:07:21

---

Be able to version control code

• [📰] Mastering Git Stash Workflow

• [📰] How to Become a Master of Git Tags

• [📰] How to track large files in Github / Bitbucket? Git LFS to the rescue

• [📰] Keep your git directory clean with git clean and git trash

• [ ] Codecademy: Learn Git

• [ ] Code School: Git Real

• [💻] Introduction to Git for Data Science

• [ ] Thoughtbot: Mastering Git

• [🅤]ꭏ GitHub & Collaboration

• [🅤]ꭏ How to Use Git and GitHub

• [🅤]ꭏ Version Control with Git

• [📺 ] 045 Introduction to Git LFS

• [📺 ] Git & Scripting

---

Be able to version control data

• [📺 ] DVC Basics

  • [ ] Versioning Data with DVC (Hands-On Tutorial!) 0:13:04
  • [ ] Sharing Data and Models with DVC (Hands-On Data Science Tutorial!) 0:08:53

• [📰] Machine Learning Ops: Data Science Version Control

• [📺 ] Data versioning in machine learning projects - Dmitry Petrov 0:34:44

• [ ] Zoom: Data versioning with DVC Part 1

• [ ] Zoom: Data versioning with DVC Part 2

---

Be able to use experiment management tools

• [📰] Supercharge your Training with Pytorch Lightning + Weights & Biases

• [📰] Storing Metadata from Machine Learning Experiments

• [📺 ] Weights and Biases Tutorial

• [📺 ] Integrate Weights & Biases with PyTorch

• [📺 ] Log (Almost) Anything with Weights & Biases

• [📺 ] Lab 5: Experiment Management (Full Stack Deep Learning - Spring 2021) 0:30:41

• [📺 ] Lecture 5: Tracking Experiments

• [📺 ] Weight & Biases

• [📺 ] SE4AI: Versioning, Provenance, and Reproducibility 1:18:29

---

Be able to setup model validation

• [📰] Evaluating a machine learning model

• [📰] Validating your Machine Learning Model

• [📰] Measuring Performance: AUPRC and Average Precision

• [📰] Measuring Performance: AUC (AUROC)

• [📰] Measuring Performance: The Confusion Matrix

• [📰] Measuring Performance: Accuracy

• [📰] ROC Curves: Intuition Through Visualization

• [📰] Precision, Recall, Accuracy, and F1 Score for Multi-Label Classification

• [📰] The Complete Guide to AUC and Average Precision: Simulations and Visualizations

• [📰] Best Use of Train/Val/Test Splits, with Tips for Medical Data

• [📰] The correct way to evaluate online machine learning models

• [📰] Proxy Metrics

• [📺 ] Accuracy as a Failure

• [📺 ] AppliedMachine Learning 2020 - 09 - Model Evaluation and Metrics 1:18:23

• [📺 ] Machine Learning Fundamentals: Cross Validation 0:06:04

• [📺 ] Machine Learning Fundamentals: The Confusion Matrix 0:07:12

• [📺 ] Machine Learning Fundamentals: Sensitivity and Specificity 0:11:46

• [📺 ] Machine Learning Fundamentals: Bias and Variance 0:06:36

• [📺 ] ROC and AUC, Clearly Explained! 0:16:26

---

Be familiar with inner working of models

Bays theorem is super interesting and applicable ==> - [📰] Naive Bayes classification

• [📰] Linear regression

• [📰] Polynomial regression

• [📰] Logistic regression

• [📰] Decision trees

• [📰] K-nearest neighbors

• [📰] Support Vector Machines

• [📰] Random forests

• [📰] Boosted trees

• [📰] Hacker's Guide to Fundamental Machine Learning Algorithms with Python

• [📰] Neural networks: activation functions

• [📰] Neural networks: training with backpropagation

• [📰] Neural Network from scratch-part 1

• [📰] Neural Network from scratch-part 2

• [📰] Perceptron to Deep-Neural-Network

• [📰] One-vs-Rest strategy for Multi-Class Classification

• [📰] Multi-class Classification — One-vs-All & One-vs-One

• [📰] One-vs-Rest and One-vs-One for Multi-Class Classification

• [📰] Deep Learning Algorithms - The Complete Guide

• [📰] Machine Learning Techniques Primer

• [ ] AWS: Understanding Neural Networks

• [📖] Grokking Deep Learning

• [📖] Make Your Own Neural Network

• [ ] Coursera: Neural Networks and Deep Learning

• [💻] Extreme Gradient Boosting with XGBoost

• [💻] Ensemble Methods in Python

• [ ] StatQuest: Machine Learning

  • [ ] StatQuest: Fitting a line to data, aka least squares, aka linear regression. 0:09:21
  • [ ] StatQuest: Linear Models Pt.1 - Linear Regression 0:27:26
  • [ ] StatQuest: StatQuest: Linear Models Pt.2 - t-tests and ANOVA 0:11:37
  • [ ] StatQuest: Odds and Log(Odds), Clearly Explained!!! 0:11:30
  • [ ] StatQuest: Odds Ratios and Log(Odds Ratios), Clearly Explained!!! 0:16:20
  • [ ] StatQuest: Logistic Regression 0:08:47
  • [ ] Logistic Regression Details Pt1: Coefficients 0:19:02
  • [ ] Logistic Regression Details Pt 2: Maximum Likelihood 0:10:23
  • [ ] Logistic Regression Details Pt 3: R-squared and p-value 0:15:25
  • [ ] Saturated Models and Deviance 0:18:39
  • [ ] Deviance Residuals 0:06:18
  • [ ] Regularization Part 1: Ridge (L2) Regression 0:20:26
  • [ ] Regularization Part 2: Lasso (L1) Regression 0:08:19
  • [ ] Ridge vs Lasso Regression, Visualized!!! 0:09:05
  • [ ] Regularization Part 3: Elastic Net Regression 0:05:19
  • [ ] StatQuest: Principal Component Analysis (PCA), Step-by-Step 0:21:57
  • [ ] StatQuest: PCA main ideas in only 5 minutes!!! 0:06:04
  • [ ] StatQuest: PCA - Practical Tips 0:08:19
  • [ ] StatQuest: PCA in Python 0:11:37
  • [ ] StatQuest: Linear Discriminant Analysis (LDA) clearly explained. 0:15:12
  • [ ] StatQuest: MDS and PCoA 0:08:18
  • [ ] StatQuest: t-SNE, Clearly Explained 0:11:47
  • [ ] StatQuest: Hierarchical Clustering 0:11:19
  • [ ] StatQuest: K-means clustering 0:08:57
  • [ ] StatQuest: K-nearest neighbors, Clearly Explained 0:05:30
  • [ ] Naive Bayes, Clearly Explained!!! 0:15:12
  • [ ] Gaussian Naive Bayes, Clearly Explained!!! 0:09:41
  • [ ] StatQuest: Decision Trees 0:17:22
  • [ ] StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data 0:05:16
  • [ ] Regression Trees, Clearly Explained!!! 0:22:33
  • [ ] How to Prune Regression Trees, Clearly Explained!!! 0:16:15
  • [ ] StatQuest: Random Forests Part 1 - Building, Using and Evaluating 0:09:54
  • [ ] StatQuest: Random Forests Part 2: Missing data and clustering 0:11:53
  • [ ] The Chain Rule 0:18:23
  • [ ] Gradient Descent, Step-by-Step 0:23:54
  • [ ] Stochastic Gradient Descent, Clearly Explained!!! 0:10:53
  • [ ] AdaBoost, Clearly Explained 0:20:54
  • [⨊ ] Part 1: Regression Main Ideas 0:15:52
  • [⨊ ] Part 2: Regression Details 0:26:45
  • [⨊ ] Part 3: Classification 0:17:02
  • [⨊ ] Part 4: Classification Details 0:36:59
  • [⨊ ] Support Vector Machines, Clearly Explained!!! 0:20:32
  • [ ] Support Vector Machines Part 2: The Polynomial Kernel 0:07:15
  • [ ] Support Vector Machines Part 3: The Radial (RBF) Kernel 0:15:52
  • [ ] XGBoost Part 1: Regression 0:25:46
  • [ ] XGBoost Part 2: Classification 0:25:17
  • [ ] XGBoost Part 3: Mathematical Details 0:27:24
  • [ ] XGBoost Part 4: Crazy Cool Optimizations 0:24:27
  • [ ] StatQuest: Fiitting a curve to data, aka lowess, aka loess 0:10:10
  • [ ] Statistics Fundamentals: Population Parameters 0:14:31
  • [ ] Principal Component Analysis (PCA) clearly explained (2015) 0:20:16
  • [ ] Decision Trees in Python from Start to Finish 1:06:23

• [🅤]ꭏ Classification Models

• [📺 ] Neural Networks from Scratch in Python

  • [ ] Neural Networks from Scratch - P.1 Intro and Neuron Code 0:16:59
  • [ ] Neural Networks from Scratch - P.2 Coding a Layer 0:15:06
  • [ ] Neural Networks from Scratch - P.3 The Dot Product 0:25:17
  • [ ] Neural Networks from Scratch - P.4 Batches, Layers, and Objects 0:33:46
  • [ ] Neural Networks from Scratch - P.5 Hidden Layer Activation Functions 0:40:05

• [📺 ] AppliedMachine Learning 2020 - 03 Supervised learning and model validation 1:12:00

• [📺 ] AppliedMachine Learning 2020 - 05 - Linear Models for Regression 1:06:54

• [📺 ] AppliedMachine Learning 2020 - 06 - Linear Models for Classification 1:07:50

• [📺 ] AppliedMachine Learning 2020 - 07 - Decision Trees and Random Forests 1:07:58

• [📺 ] AppliedMachine Learning 2020 - 08 - Gradient Boosting 1:02:12

• [📺 ] AppliedMachine Learning 2020 - 18 - Neural Networks 1:19:36

• [📺 ] AppliedMachine Learning 2020 - 12 - AutoML (plus some feature selection) 1:25:38

---

Be able to improve models

• [📰] Deep neural networks: preventing overfitting

• [📰] Normalizing your data (specifically, input and batch normalization)

• [📰] Batch Normalization

• [📰] Are Deep Neural Networks Dramatically Overfitted?

• [📰] In-layer normalization techniques for training very deep neural networks

• [📰] Label Smoothing Explained using Microsoft Excel

• [📰] Uncertainty Quantification Part 4: Leveraging Dropout in Neural Networks (CNNs)

• [📰] Simple Ways to Tackle Class Imbalance

• [📺 ] AppliedMachine Learning 2020 - 10 - Calibration, Imbalanced data 1:16:14

• [📺 ] Lecture 10: Troubleshooting Deep Neural Networks

---

Be familiar with fundamental Machine Learning concepts

CNN

• [📰] Connections: Log Likelihood, Cross Entropy, KL Divergence, Logistic Regression, and Neural Networks

• [📰] MLE and MAP — in layman’s terms

• [ ] An overview of gradient descent optimization algorithms

• [📰] Optimization for Deep Learning Highlights in 2017

• [📰] Gradient descent

• [📰] Setting the learning rate of your neural network

• [📰] Cross-entropy for classification

• [📰] Dismantling Neural Networks to Understand the Inner Workings with Math and Pytorch

• [💻] AI Fundamentals

• [💻] Foundations of Predictive Analytics in Python (Part 1)

• [💻] Foundations of Predictive Analytics in Python (Part 2)

• [ ] Elements of AI

• [ ] edX: Principles of Machine Learning

• [ ] edX: Data Science Essentials

• [ 📺] Fast.ai: Deep Learning for Coder (2020)

  • [ 📺] Lesson 0
  • [📺 ] Lesson 1
  • [📺 ] Lesson 2
  • [📺 ] Lesson 3
  • [📺 ] Lesson 4
  • [ 📺] Lesson 5
  • [ 📺] Lesson 6
  • [ 📺] Lesson 7
  • [📺 ] Lesson 8

• [📺 ] Deep Double Descent

---

Implement models in scikit-learn

• [📰] Stacking made easy with Sklearn

• [📰] Curve Fitting With Python

• [📰] A Guide to Calibration Plots in Python

• [ ] Calmcode: human-learn

• [💻] Supervised Learning with scikit-learn

• [💻] Machine Learning with Tree-Based Models in Python

• [💻] Introduction to Linear Modeling in Python

• [💻] Linear Classifiers in Python

• [💻] Generalized Linear Models in Python

• [ ] Notebook: scikit-learn tips

• [ ] Pluralsight: Building Machine Learning Models in Python with scikit-learn

• [ ] Video: human learn

• [📺 ] dabl: Automatic Machine Learning with a Human in the Loop 00:25:43

• [📺 ] Multilabel and Multioutput Classification -Machine Learning with TensorFlow & scikit-learn on Python

• [📺 ] DABL: Automatic machine learning with a human in the loop- AI Latim American SumMIT Day 1

---

Be able to implement models in Tensorflow and Keras

• [ ] Coursera: Introduction to Tensorflow

• [ ] Coursera: Convolutional Neural Networks in TensorFlow

• [ ] Deeplizard: Keras - Python Deep Learning Neural Network API

• [📖] Deep Learning with Python (Page: 276)

• [💻] Deep Learning in Python

• [💻] Convolutional Neural Networks for Image Processing

• [💻] Introduction to TensorFlow in Python

• [💻] Introduction to Deep Learning with Keras

• [💻] Advanced Deep Learning with Keras

• [G] Machine Learning Crash Course

• [ ] Pluralsight: Deep Learning with Keras

• [🅤]ꭏ Intro to TensorFlow for Deep Learning

---

Be able to implement models in PyTorch

• [📰] Keeping Up with PyTorch Lightning and Hydra

• [📰] The One PyTorch Trick Which You Should Know

• [📰] How does automatic differentiation really work?

• [📰] 7 Tips To Maximize PyTorch Performance

• [📰] An introduction to PyTorch Lightning with comparisons to PyTorch

• [📰] Converting From Keras To PyTorch Lightning

• [📰] From PyTorch to PyTorch Lightning — A gentle introduction

• [📰] Introducing PyTorch Lightning Sharded: Train SOTA Models, With Half The Memory

• [📰] Sharded: A New Technique To Double The Size Of PyTorch Models

• [📰] Understanding Bidirectional RNN in PyTorch

• [📰] A developer-friendly guide to mixed precision training with PyTorch

• [📰] A developer-friendly guide to model pruning in PyTorch

• [📰] A developer-friendly guide to model quantization with PyTorch

• [📰] Tricks for training PyTorch models to convergence more quickly

• [📰] PyTorch Lightning Bolts — From Linear, Logistic Regression on TPUs to pre-trained GANs

• [📰] Scaling Logistic Regression Via Multi-GPU/TPU Training

• [📰] Training Neural Nets on Larger Batches: Practical Tips for 1-GPU, Multi-GPU & Distributed setups

• [📰] PyTorch Lightning 0.9 — synced BatchNorm, DataModules and final API!

• [📰] PyTorch Lightning: Metrics

• [📰] PyTorch Multi-GPU Metrics Library and More in PyTorch Lightning 0.8.1

• [📰] Distributed model training in PyTorch using DistributedDataParallel

• [📰] Distributed model training in PyTorch using DistributedDataParallel

• [📰] EINSUM IS ALL YOU NEED - EINSTEIN SUMMATION IN DEEP LEARNING

• [📰] Faster Deep Learning Training with PyTorch – a 2021 Guide

• [📰] Fit More and Train Faster With ZeRO via DeepSpeed and FairScale

• [📰] PyTorch Lightning V1.2.0- DeepSpeed, Pruning, Quantization, SWA

• [📰] But what are PyTorch DataLoaders really?

• [📰] Using PyTorch + NumPy? You're making a mistake.

• [📰] How Wadhwani AI Uses PyTorch To Empower Cotton Farmers

• [📰] Taming LSTMs: Variable-sized mini-batches and why PyTorch is good for your health

• [📰] How to Build a Streaming DataLoader with PyTorch

• [📰] Transform your ML-model to Pytorch with Hummingbird

• [📰] PyTorch Loss Functions: The Ultimate Guide

• [📰] Pad pack sequences for Pytorch batch processing with DataLoader

• [📰] Model Parallelism

• [ ] Notebook: Tensor Arithmetic

• [ ] Notebook: Autograd

• [ ] Notebook: Optimization

• [ ] Notebook: Network modules

• [ ] Notebook: Datasets and Dataloaders

• [ ] Documentation: Pytorch Lightning

• [💻] Introduction to Deep Learning with PyTorch

• [ ] Deeplizard: Neural Network Programming - Deep Learning with PyTorch

• [🅤]ꭏ Intro to Deep Learning with PyTorch

• [📺 ] PyTorch Lightning 101

  • [ ] Training a classification model on MNIST with PyTorch
  • [ ] From PyTorch to PyTorch Lightning
  • [ ] Lightning Data Modules
  • [ ] PyTorch Dropout, Batch size and interactive debugging
  • [ ] Episode 4: Implementing a PyTorch Trainer: PyTorch Lightning Trainer and callbacks under-the-hood

• [📺 ] SimCLR with PyTorch Lightning

• [📺 ] PyTorch Performance Tuning Guide 26:41:00

• [📺 ] Skin Cancer Detection with PyTorch

  • [ ] [PART 1] Skin Cancer Detection with PyTorch 0:10:21
  • [ ] [PART 2] Skin Cancer Detection with PyTorch 0:21:57
  • [ ] [PART 3] Skin Cancer Detection with PyTorch 0:22:24

• [📺 ] Learn with Lightning

• [📺 ] PyTorch Tutorial - RNN & LSTM & GRU - Recurrent Neural Nets 00:15:51

• [📺 ] Pytorch Zero to All

  • [ ] PyTorch Lecture 01: Overview 0:10:18
  • [ ] PyTorch Lecture 02: Linear Model 0:12:52
  • [ ] PyTorch Lecture 03: Gradient Descent 0:08:24
  • [ ] PyTorch Lecture 04: Back-propagation and Autograd 0:15:25
  • [ ] PyTorch Lecture 05: Linear Regression in the PyTorch way 0:11:50
  • [ ] PyTorch Lecture 06: Logistic Regression 0:10:41
  • [ ] PyTorch Lecture 07: Wide and Deep 0:10:37
  • [ ] PyTorch Lecture 08: PyTorch DataLoader 0:06:41
  • [ ] PyTorch Lecture 09: Softmax Classifier 0:18:47
  • [ ] PyTorch Lecture 10: Basic CNN 0:15:52
  • [ ] PyTorch Lecture 11: Advanced CNN 0:12:58
  • [ ] PyTorch Lecture 12: RNN1 - Basics 0:28:47
  • [ ] PyTorch Lecture 13: RNN 2 - Classification 0:17:22

• [ ] PyTorch Developer Day 2020 | Full Livestream

• [📺 ] Lightning Chat: How a Grandmaster Won a Kaggle Competition Using Pytorch Lightning

• [📺 ] Production Inference Deployment with PyTorch

• [📺 ] What is Automatic Differentiation?

• [📺 ] JAX: accelerated machine learning research via composable function transformations in Python

---

Be able to implement models using cloud services

• [ ] AWS: Amazon Transcribe Deep Dive: Using Feedback Loops to Improve Confidence Level of Transcription

• [ ] AWS: Build a Text Classification Model with AWS Glue and Amazon SageMaker

• [ ] AWS: Deep Dive on Amazon Rekognition: Building Computer Visions Based Smart Applications

• [ ] AWS: Hands-on Rekognition: Automated Video Editing

• [ ] AWS: Introduction to Amazon Comprehend

• [ ] AWS: Introduction to Amazon Comprehend Medical

• [ ] AWS: Introduction to Amazon Elastic Inference

• [ ] AWS: Introduction to Amazon Forecast

• [ ] AWS: Introduction to Amazon Lex

• [ ] AWS: Introduction to Amazon Personalize

• [ ] AWS: Introduction to Amazon Polly

• [ ] AWS: Introduction to Amazon SageMaker Ground Truth

• [ ] AWS: Introduction to Amazon SageMaker Neo

• [ ] AWS: Introduction to Amazon Transcribe

• [ ] AWS: Introduction to Amazon Translate

• [ ] AWS: Introduction to AWS Marketplace - Machine Learning Category

• [ ] AWS: Machine Learning Exam Basics

• [ ] AWS: Neural Machine Translation with Sockeye

• [ ] AWS: Process Model: CRISP-DM on the AWS Stack

• [ ] AWS: Satellite Image Classification in SageMaker

• [💻] Introduction to AWS Boto in Python

• [ ] edX: Amazon SageMaker: Simplifying Machine Learning Application Development

---

Be able to apply unsupervised learning algorithms

• [📰] Decrypt Generative Adversarial Networks (GAN)

• [📰] GANs in computer vision - Conditional image synthesis and 3D object generation

• [📰] GANs in computer vision - Improved training with Wasserstein distance, game theory control and progressively growing schemes

• [📰] GANs in computer vision - Introduction to generative learning

• [📰] GANs in computer vision - self-supervised adversarial training and high-resolution image synthesis with style incorporation

• [📰] GANs in computer vision - semantic image synthesis and learning a generative model from a single image

• [📰] Paper Summary: DeViSE: A Deep Visual-Semantic Embedding Model

• [📰] Contrastive Self-Supervised Learning

• [📰] From Autoencoder to Beta-VAE

• [📰] Self-Supervised Representation Learning

• [📰] Self-supervised learning: The dark matter of intelligence

• [📰] Understanding self-supervised and contrastive learning with "Bootstrap Your Own Latent" (BYOL)

• [📰] How to Generate Images using Autoencoders

• [📰] Introduction to autoencoders

• [📰] Soft clustering with Gaussian mixed models (EM)

• [📰] Variational autoencoders

• [📰] Build a simple Image Retrieval System with an Autoencoder

• [📰] Deep Inside Autoencoders

• [📰] An overview of proxy-label approaches for semi-supervised learning

• [📰] From Research to Production with Deep Semi-Supervised Learning

• [📰] Affinity Propagation Algorithm Explained

• [📰] Algorithm Breakdown: Affinity Propagation

• [📰] Create, Visualize and Interpret Customer Segments

• [📰] A Framework For Contrastive Self-Supervised Learning And Designing A New Approach

• [📰] EfficientDet Meets Pytorch-Lightning

• [📰] Principal components analysis (PCA)

• [📰] RecSys 2020 - Takeaways and Notable Papers

• [📰] Grouping data points with k-means clustering

• [📰] A gentle introduction to HDBSCAN and density-based clustering

• [📰] Deepfakes: Face synthesis with GANs and Autoencoders

• [📰] Unsupervised Learning of Visual Features by Contrasting Cluster Assignments

• [ ] Berkeley: Deep Unsupervised Learning Spring 2020

  • [ ] L1 Introduction -- CS294-158-SP20 Deep Unsupervised Learning -- UC Berkeley, Spring 2020 1:10:02
  • [ ] L2 Autoregressive Models -- CS294-158-SP20 Deep Unsupervised Learning -- UC Berkeley, Spring 2020 2:27:23
  • [ ] L3 Flow Models -- CS294-158-SP20 Deep Unsupervised Learning -- UC Berkeley -- Spring 2020 1:56:53
  • [ ] L4 Latent Variable Models (VAE) -- CS294-158-SP20 Deep Unsupervised Learning -- UC Berkeley 2:19:33
  • [ ] Lecture 5 Implicit Models -- GANs Part I --- UC Berkeley, Spring 2020 2:32:32
  • [ ] Lecture 6 Implicit Models / GANs part II --- CS294-158-SP20 Deep Unsupervised Learning -- Berkeley 2:09:14
  • [ ] Lecture 7 Self-Supervised Learning -- UC Berkeley Spring 2020 - CS294-158 Deep Unsupervised Learning 2:20:41
  • [ ] L8 Round-up of Strengths and Weaknesses of Unsupervised Learning Methods -- UC Berkeley SP20 0:41:51
  • [ ] L9 Semi-Supervised Learning and Unsupervised Distribution Alignment -- CS294-158-SP20 UC Berkeley 2:16:00
  • [ ] L10 Compression -- UC Berkeley, Spring 2020, CS294-158 Deep Unsupervised Learning 3:09:49
  • [ ] L11 Language Models -- guest instructor: Alec Radford (OpenAI) --- Deep Unsupervised Learning SP20 2:38:19
  • [ ] L12 Representation Learning for Reinforcement Learning --- CS294-158 UC Berkeley Spring 2020 2:01:56

• [💻] Customer Segmentation in Python

• [💻] Unsupervised Learning in Python

• [ ] Deck: Demystifying Self-Supervised Learning for Visual Recognition

• [ ] DeepMind: Inefficient Data Efficiency

• [G] Clustering

• [🅤]ꭏ Segmentation and Clustering

• [ ] Wandb: Unsupervised Visual Representation Learning with SwAV

• [📺 ] AppliedMachine Learning 2020 - 14 - Clustering and Mixture Models 1:26:33

• [📺 ] AppliedMachine Learning 2020 - 13 - Dimensionality reduction 1:30:34

• [📺 ] BYOL: Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning (Paper Explained)

• [📺 ] A critical analysis of self-supervision, or what we can learn from a single image (Paper Explained)

• [📺 ] Week 10 – Lecture: Self-supervised learning (SSL) in computer vision (CV)

• [📺 ] CVPR 2020 Tutorial: Towards Annotation-Efficient Learning

• [📺 ] Yuki Asano | Self-Supervision | Self-Labelling | Labelling Unlabelled videos | CV | CTDS.Show #81

• [📺 ] Contrastive Clustering with SwAV

• [📺 ] Variational Autoencoders - EXPLAINED! 0:17:36

• [📺 ] OptaProAnalyticsForum– Learning to watch football: Self-supervised representations for tracking data

• [📺 ] Can a Neural Net tell if an image is mirrored? – Visual Chirality

• [📺 ] Deep InfoMax: Learning deep representations by mutual information estimation and maximization

• [ ] Deep Learning Lecture Summer 2020

  • [ ] Deep Learning: Unsupervised Learning - Part 1
  • [ ] Deep Learning: Unsupervised Learning - Part 2
  • [ ] Deep Learning: Unsupervised Learning - Part 3
  • [ ] Deep Learning: Unsupervised Learning - Part 4
  • [ ] Deep Learning: Unsupervised Learning - Part 5
  • [ ] Deep Learning: Weakly and Self-Supervised Learning - Part 1
  • [ ] Deep Learning: Weakly and Self-Supervised Learning - Part 2
  • [ ] Deep Learning: Weakly and Self-Supervised Learning - Part 3
  • [ ] Deep Learning: Weakly and Self-Supervised Learning - Part 4

• [ ] ECCV 2020: New Frontiers for Learning with Limited Labels or Data

  • [ ] Introduction to New Frontiers on Learning with Limited Labels or Data
  • [ ] Self-Supervised Part and Viewpoint Discovery from Image Collections
  • [ ] Learning Visual Correspondences across Instances and Video Frames
  • [ ] Limitless Labels in a Labelless World: Weak Supervision with Noisy Labels

• [📺 ] Self-Supervised Learning - What is Next? - Workshop at ECCV 2020, August 28th

  • [ ] Next Challenges for Self-Supervised Learning - Aäron van den Oord 0:20:13
  • [ ] Perspectives on Unsupervised Representation Learning - Paolo Favaro 0:42:41
  • [ ] Learning and Transferring Visual Representations with Few Labels - Carl Doersch 0:32:53
  • [ ] Self-Supervision as a Path to a Post-Dataset Era - Alexei Alyosha Efros 0:38:06

• [📺 ] Marco Cuturi - A Primer on Optimal Transport

• [📺 ] Sebastian Ruder: Neural Semi-supervised Learning under Domain Shift

• [📺 ] Clustering Algorithms

---

Be able to implement NLP models

• [📰] Fixing common Unicode mistakes with Python – after they’ve been made

• [📰] 10 Popular Keyword Extraction Algorithms in Natural Language Processing

• [📰] Deconstructing BERT

• [📰] How To Create Data Products That Are Magical Using Sequence-to-Sequence Models

• [📰] Why Rasa uses Sparse Layers in Transformers

• [📰] Semantic Search On Documents

• [📰] Locality-sensitive Hashing and Singular to Plural Noun Conversion

• [📰] Build A Keyword Extraction API with Spacy, Flask, and FuzzyWuzzy

• [📰] What is Hidden in the Hidden Markov Model?

• [📰] Unsupervised NER using BERT

• [📰] Unsupervised creation of interpretable sentence representations

• [📰] Unsupervised synonym harvesting

• [📰] Zero shot NER using RoBERTA

• [📰] Maximizing BERT model performance

• [📰] Swiss army knife for unsupervised task solving

• [📰] 10 Exciting Ideas of 2018 in NLP

• [📰] 74 Summaries of Machine Learning and NLP Research

• [📰] Advance BERT model via transferring knowledge from Cross-Encoders to Bi-Encoders

• [📰] Learning to select data for transfer learning

• [📰] T5 — a model that explores the limits of transfer learning

• [📰] The State of Transfer Learning in NLP

• [📰] Haystack: The State of Search in 2021

• [📰] How to build a State-of-the-Art Conversational AI with Transfer Learning

• [📰] Commonsense Reasoning for Natural Language Processing

• [📰] Language Models

• [📰] Paraphrasing

• [📰] Poor man’s GPT-3: Few shot text generation with T5 Transformer

• [📰] Text Generation

• [📰] Controlling Text Generation with Plug and Play Language Models

• [📰] What makes a good conversation?

• [📰] How to steal modern NLP systems with gibberish?

• [📰] Intuition & Use-Cases of Embeddings in NLP & beyond

• [📰] The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning)

• [📰] The Illustrated GPT-2 (Visualizing Transformer Language Models)

• [📰] The Illustrated Transformer

• [📰] The Illustrated Word2vec

• [📰] A Deep Dive into the Reformer

• [📰] A Survey of Long-Term Context in Transformers

• [📰] Large Memory Layers with Product Keys

• [📰] Optimal Transport and the Sinkhorn Transformer

• [📰] Pattern-Exploiting Training

• [📰] Talking-Heads Attention

• [📰] How to Apply BERT to Arabic and Other Languages

• [📰] Neural Language Models as Domain-Specific Knowledge Bases

• [📰] Domain-Specific BERT Models

• [📰] Existing Tools for Named Entity Recognition

• [📰] Smart Batching Tutorial - Speed Up BERT Training

• [📰] Attention? An Other Perspective!: Part 2

• [📰] Attention? An Other Perspective!: Part 3

• [📰] Attention? An Other Perspective!: Part 4

• [📰] Attention? An Other Perspective!: Part 5

• [📰] Attention? An Other Perspective!: Part 1

• [📰] Rebuilding the spellchecker, pt.4: Introduction to suggest algorithm

• [📰] Rebuilding the spellchecker: Hunspell and the order of edits

• [📰] Rebuilding the most popular spellchecker. Part 1

• [📰] Rebuilding the spellchecker, pt.2: Just look in the dictionary, they said!

• [📰] Rebuilding the spellchecker, pt.3: Lookup—compounds and solutions

• [📰] Automatic Topic Labeling in 2018: History and Trends

• [📰] Deep Learning for NLP Best Practices

• [📰] Attention and Memory in Deep Learning and NLP

• [📰] Long Short-Term Memory: From Zero to Hero with PyTorch

• [📰] Ten trends in Deep learning NLP

• [📰] The Illustrated Wav2vec

• [📰] A Review of the Neural History of Natural Language Processing

• [📰] ColumnTransformer Meets Natural Language Processing

• [📰] Neural Transfer Learning for Natural Language Processing

• [📰] Tracking the Progress in Natural Language Processing

• [📰] Implementing Bengio’s Neural Probabilistic Language Model (NPLM) using Pytorch

• [📰] Leveraging Pre-trained Language Model Checkpoints for Encoder-Decoder Models

• [📰] How many data points is a prompt worth?

• [📰] NLP: Pre-trained Sentiment Analysis

• [📰] Transformer-based Encoder-Decoder Models

• [📰] Understanding BigBird's Block Sparse Attention"

• [📰] Interactive Topic Modeling with BERTopic

• [📰] Understanding Climate Change Domains through Topic Modeling

• [📰] When Topic Modeling is Part of the Text Pre-processing

• [📰] Keyword Extraction with BERT

• [📰] Topic Modeling with BERT

• [📰] Question Classification using Self-Attention Transformer — Part 1.1

• [📰] Question Classification using Self-Attention Transformer — Part 1

• [📰] Question Classification using Self-Attention Transformer — Part 2

• [📰] Question Classification using Self-Attention Transformer — Part 3

• [📰] Generalized Language Models

• [📰] Learning Word Embedding

• [📰] Reducing Toxicity in Language Models

• [📰] The Transformer Family

• [📰] DialogRPT with Huggingface Transformers

• [📰] Hugging Face Reads - 01/2021 - Sparsity and Pruning

• [📰] Hugging Face Reads, Feb. 2021 - Long-range Transformers

• [📰] Porting fairseq wmt19 translation system to transformers

• [📰] On word embeddings - Part 1

• [📰] On word embeddings - Part 2: Approximating the Softmax

• [📰] On word embeddings - Part 3: The secret ingredients of word2vec

• [📰] Word embeddings in 2017: Trends and future direction

• [📰] DaCy: New Fast and Efficient State-of-the-Art in Danish NLP!

• [📰] State-of-the-Art Language Models in 2020

• [📰] Machine Learning and NLP Publications in 2020

• [📰] Zero-Shot Learning in Modern NLP

• [📰] Introduction to recurrent neural networks

• [📰] Understanding LSTM Networks

• [📰] Explaining RNNs without neural networks

• [📰] Understanding Convolutional Neural Networks for NLP

• [📰] Search (Pt 1) — A Gentle Introduction

• [📰] Search (Pt 2) — A Semantic Horse Race

• [📰] Search (Pt 3) — Elastic Transformers

• [📰] Improved Few-Shot Text classification

• [📰] Text classification from few training examples

• [📰] Multi-Label Text Classification

• [📰] Semantic search using BERT embeddings

• [📰] What Semantic Search Can do for You

• [📰] How To Create Natural Language Semantic Search For Arbitrary Objects With Deep Learning

• [📰] How to Implement a Beam Search Decoder for Natural Language Processing

• [📰] Creating a class-based TF-IDF with Scikit-Learn

• [📰] String Matching with BERT, TF-IDF, and more!

• [📰] How to Use n-gram Models to Detect Format Errors in Datasets

• [📰] The Unreasonable Effectiveness of Recurrent Neural Networks

• [📰] A review of BERT based models

• [📰] Document clustering

• [📰] Document search with fragment embeddings

• [ ] Doc: Huggingface Summary of the models

• [ ] Doc: Summary of the tokenizers

• [📰] GPT-2 A nascent transfer learning method that could eliminate supervised learning in some NLP tasks

• [📰] Evaluation Metrics for Language Modeling

• [📰] Representation Learning and Retrieval

• [📰] A survey of cross-lingual word embedding models

• [📰] Unsupervised Cross-lingual Representation Learning

• [📰] Spelling Correction: How to make an accurate and fast corrector

• [📰] Speller100: Zero-shot spelling correction at scale for 100-plus languages

• [📰] Understanding BERT’s Semantic Interpretations

• [📰] Using NLP (BERT) to improve OCR accuracy

• [📰] Brief review of word embedding families (2019)

• [📰] Trends in input representation for state-of-art NLP models (2019)

• [📰] An Overview of Multi-Task Learning in Deep Neural Networks

• [📰] Multi-Task Learning Objectives for Natural Language Processing

• [📰] GPU Benchmarks for Fine-Tuning BERT

• [📰] Recent Advances in Language Model Fine-tuning

• [📰] The Current Best of Universal Word Embeddings and Sentence Embeddings

• [📰] Topic Modeling for Keyword Extraction

• [📰] Understanding ARPA and Language Models

• [📰] Gaussian Mixture Models for Clustering

• [📰] Explain NLP models with LIME & SHAP

• [📰] How to solve 90% of NLP problems: a step-by-step guide

• [📰] Does GPT-2 Know Your Phone Number?

• [📰] How to Outperform GPT-3 by Combining Task Descriptions With Supervised Learning

• [📰] LSTM Primer With Real Life Application( DeepMind Kidney Injury Prediction )*

• [📰] T5 — XLNet — a clever language modeling solution

• [📰] Using an NLP Q&A System To Study Climate Hazards and Nature-Based Solutions

• [📰] Hyperparameter Optimization for 🤗Transformers: A guide

• [📰] How To Do Things With Words. And Counters

• [📰] Automatically Summarize Trump’s State of the Union Address

• [📰] Solving NER with BERT for any entity type with very little training data (compared to past approaches)

• [📰] 10 Things You Need to Know About BERT and the Transformer Architecture That Are Reshaping the AI Landscape

• [📰] Semantic Entailment

• [📰] Shrinking fastText embeddings so that it fits Google Colab

• [📰] Fuzzy Matching/Fuzzy Logic Explained

• [📰] Under the Hood of RNNs

• [📰] All Our N-gram are Belong to You

• [📰] Perplexity Intuition (and its derivation)

• [📰] Part of Speech Tagging with Hidden Markov Chain Models

• [📰] NLP Year In Review

• [📰] UNDERSTANDING WORD2VEC THROUGH CULTURAL DIMENSIONS

• [📰] Exploring LSTMs

• [📰] Aspect-Based Opinion Mining (NLP with Python)

• [📰] pyLDAvis: Topic Modelling Exploration Tool That Every NLP Data Scientist Should Know

• [📰] Machine Learning and NLP Research Highlights of 2020

• [📰] Introducing spaCy

• [📰] 3 subword algorithms help to improve your NLP model performance

• [📰] Examining BERT’s raw embeddings

• [📰] Making sense of LSTMs by example

• [📰] The Transformer Explained

• [📰] Understanding building blocks of ULMFIT

• [📰] Building a sentence embedding index with fastText and BM25

• [📰] The Annotated GPT-2

• [📰] Key topics extraction and contextual sentiment of users reviews

• [📰] Google mT5 multilingual text-to-text transformer: A Brief Paper Analysis

• [📰] Building RNNs is Fun with PyTorch and Google Colab

• [📰] Faster and smaller quantized NLP with Hugging Face and ONNX Runtime

• [📰] Visualizing A Neural Machine Translation Model (Mechanics of Seq2seq Models With Attention)

• [📰] How I Used Deep Learning To Train A Chatbot To Talk Like Me (Sorta)

• [📰] Generating Questions Using Transformers

• [📰] Feature-based Approach with BERT

• [📰] Performers: The Kernel Trick, Random Fourier Features, and Attention

• [📰] Text Similarities : Estimate the degree of similarity between two texts

• [📰] NLP's ImageNet moment has arrived

• [📰] Simple PyTorch Transformer Example with Greedy Decoding

• [📰] Character level language model RNN

• [📰] How we used Universal Sentence Encoder and FAISS to make our search 10x smarter

• [📰] Adapting Text Augmentation to Industry problems

• [📰] The Annotated Transformer

• [📰] OpenAI's GPT-3 Language Model: A Technical Overview

• [📰] NLP for Supervised Learning - A Brief Survey

• [📰] The 4 Biggest Open Problems in NLP

• [📰] How GPT3 Works

• [📰] Why You Should Do NLP Beyond English

• [📰] Breaking the spell of the spelling check

• [📰] How to Write a Spelling Corrector

• [📰] Spellchecking by computer

• [📰] A Spellchecker Used to Be a Major Feat of Software Engineering

• [📰] 1000x Faster Spelling Correction algorithm (2012)

• [📰] The Pruning Radix Trie — a Radix Trie on steroids

• [📰] Text Data Cleanup - Dynamic Embedding Visualisation

• [📰] Rotary Embeddings: A Relative Revolution

• [📰] Using embeddings to help find similar restaurants in Search

• [📰] Evolution of and experiments with feed ranking at Swiggy

• [📰] Personalizing Swiggy POP Recommendations

• [📰] Fan(s)tastic: Search for blazing-fast results

• [📰] Find My Food: Semantic Embeddings for Food Search Using Siamese Networks

• [📰] Learning To Rank Restaurants

• [📰] Is Word Sense Disambiguation outdated?

• [📰] Named-Entity evaluation metrics based on entity-level

• [📰] Comparison Of Ngram Fuzzy Matching Approaches

• [📰] String similarity — the basic know your algorithms guide!

• [📰] Evolution of Word to Vector

• [ ] A friendly introduction to Recurrent Neural Networks

• [📖] Embeddings in Natural Language Processing

• [📖] Linguistic Fundamentals for Natural Language Processing: 100 Essentials from Morphology and Syntax

• [ ] Coursera: Sequence Models

• [ ] Coursera: Natural Language Processing in TensorFlow

• [ ] CMU: Low-resource NLP Bootcamp 2020

  • [ ] CMU Low resource NLP Bootcamp 2020 (1): NLP Tasks 1:46:06
  • [ ] CMU Low resource NLP Bootcamp 2020 (2): Linguistics - Phonology and Morphology 1:24:08
  • [ ] CMU Low resource NLP Bootcamp 2020 (3): Machine Translation 1:55:59
  • [ ] CMU Low resource NLP Bootcamp 2020 (4): Linguistics - Syntax and Morphosyntax 2:00:21
  • [ ] CMU Low resource NLP Bootcamp 2020 (5): Neural Representation Learning 1:19:57
  • [ ] CMU Low resource NLP Bootcamp 2020 (6): Multilingual NLP 2:04:34
  • [ ] CMU Low resource NLP Bootcamp 2020 (7): Speech Synthesis 2:22:14

• [ ] CMU: Neural Nets for NLP 2021

  • [ ] CMU Neural Nets for NLP 2021 (1): Introduction 1:22:40
  • [ ] CMU Neural Nets for NLP 2021 (2): Language Modeling, Efficiency/Training Tricks 0:58:24
  • [ ] CMU Neural Nets for NLP 2021 (3): Building A Neural Network Toolkit for NLP, minnn 0:34:42
  • [ ] CMU Neural Nets for NLP 2021 (4): Efficiency Tricks for Neural Nets 0:43:28
  • [ ] CMU Neural Nets for NLP 2021 (5): Recurrent Neural Networks 0:38:50
  • [ ] CMU Neural Nets for NLP 2021 (6): Conditioned Generation 0:45:06
  • [ ] CMU Neural Nets for NLP 2021 (7): Attention 0:38:23
  • [ ] CMU Neural Nets for NLP 2021 (8): Distributional Semantics and Word Vectors 0:42:44
  • [ ] CMU Neural Nets for NLP 2021 (9): Sentence and Contextual Word Representations 0:50:53
  • [ ] CMU Neural Nets for NLP 2021 (11): Structured Prediction with Local Independence Assumptions 0:36:43
  • [ ] CMU Neural Nets for NLP 2021 (10): Debugging Neural Nets (for NLP) 0:43:58
  • [ ] CMU Neural Nets for NLP 2021 (12): Model Interpretation 0:28:52
  • [ ] CMU Neural Nets for NLP 2021 (13): Generating Trees and Graphs 0:41:05
  • [ ] CMU Neural Nets for NLP 2021 (14): Margin-based and Reinforcement Learning for Structured Prediction 0:47:20
  • [ ] CMU Neural Nets for NLP 2021 (15): Sequence-to-sequence Pre-training 0:27:22
  • [ ] CMU Neural Nets for NLP 2021 (16): Machine Reading w/ Neural Nets 0:43:08
  • [ ] CMU Neural Nets for NLP 2021 (17): Neural Nets + Knowledge Bases 0:44:19
  • [ ] CMU Neural Nets for NLP 2021 (18): Advanced Search Algorithms 0:47:58
  • [ ] CMU Neural Nets for NLP 2021 (19): Adversarial Methods 0:41:56
  • [ ] CMU Neural Nets for NLP 2021 (20): Models w/ Latent Random Variables 0:41:06
  • [ ] CMU Neural Nets for NLP 2021 (21): Multilingual Learning 0:33:10
  • [ ] CMU Neural Nets for NLP 2021 (22): Bias in NLP 0:32:44
  • [ ] CMU Neural Nets for NLP 2021 (23): Document-level Models 0:40:04

• [ ] CMU Multilingual NLP 2020

  • [ ] CMU Multilingual NLP 2020 (1): Introduction 1:17:29
  • [ ] CMU Multilingual NLP 2020 (2): Typology - The Space of Language 0:37:13
  • [ ] CMU Multilingual NLP 2020 (3): Words, Parts of Speech, Morphology 0:38:58
  • [ ] CMU Multilingual NLP 2020 (4): Text Classification and Sequence Labeling 0:45:56
  • [ ] CMU Multilingual NLP 2020 (5): Advanced Text Classification/Labeling 0:49:40
  • [ ] CMU Multilingual NLP 2020 (6): Translation, Evaluation, and Datasets 0:46:17
  • [ ] CMU Multilingual NLP 2020 (7): Machine Translation/Sequence-to-sequence Models 0:43:51
  • [ ] CMU Multilingual NLP 2020 (8): Data Augmentation for Machine Translation 0:24:42
  • [ ] CMU Multilingual NLP 2020 (9): Language Contact and Similarity Across Languages 0:30:25
  • [ ] CMU Multilingual NLP 2020 (10): Multilingual Training and Cross-lingual Transfer 0:39:58
  • [ ] CMU Multilingual NLP 2020 (11): Unsupervised Translation 0:51:17
  • [ ] CMU Multilingual NLP 2020 (12): Code Switching, Pidgins, and Creoles 0:46:37
  • [ ] CMU Multilingual NLP 2020 (13): Speech 0:41:16
  • [ ] CMU Multilingual NLP 2020 (14): Automatic Speech Recognition 0:39:33
  • [ ] CMU Multilingual NLP 2020 (15): Low Resource ASR 0:43:38
  • [ ] CMU Multilingual NLP 2020 (16): Text to Speech 0:39:00
  • [ ] CMU Multilingual NLP 2020 (17): Morphological Analysis and Inflection 0:45:22
  • [ ] CMU Multilingual NLP 2020 (18): Dependency Parsing 0:38:15
  • [ ] CMU Multilingual NLP 2020 (19): Data Annotation 0:53:08
  • [ ] CMU Multilingual NLP 2020 (20): Active Learning 0:28:37
  • [ ] CMU Multilingual NLP 2020 (21): Information Extraction 0:41:00
  • [ ] CMU Multilingual NLP 2020 (22): Multilingual NLP for Indigenous Languages 1:21:58
  • [ ] CMU Multilingual NLP 2020 (23): Universal Translation at Scale 1:27:33

• [ ] CS685: Advanced Natural Language Processing

  • [ ] UMass CS685 (Advanced NLP): Attention mechanisms 0:48:53
  • [ ] UMass CS685 (Advanced NLP): Question answering 0:59:50
  • [ ] UMass CS685 (Advanced NLP): Better BERTs 0:52:23
  • [ ] UMass CS685 (Advanced NLP): Text generation decoding and evaluation 1:02:32
  • [ ] UMass CS685 (Advanced NLP): Paraphrase generation 1:10:59
  • [ ] UMass CS685 (Advanced NLP): Crowdsourced text data collection 0:58:31
  • [ ] UMass CS685 (Advanced NLP): Model distillation and security threats 1:09:25
  • [ ] UMass CS685 (Advanced NLP): Retrieval-augmented language models 0:52:13
  • [ ] UMass CS685 (Advanced NLP): Implementing a Transformer 1:12:36
  • [ ] UMass CS685 (Advanced NLP): vision + language 1:06:28
  • [ ] UMass CS685 (Advanced NLP): exam review 1:24:36
  • [ ] UMass CS685 (Advanced NLP): Intermediate fine-tuning 1:10:35
  • [ ] UMass CS685 (Advanced NLP): ethics in NLP 0:56:57
  • [ ] UMass CS685 (Advanced NLP): probe tasks 0:54:30
  • [ ] UMass CS685 (Advanced NLP): semantic parsing 0:48:49
  • [ ] UMass CS685 (Advanced NLP): commonsense reasoning (guest lecture by Lorraine Li) 0:58:53

• [💻] Advanced NLP with spaCy

• [💻] Building Chatbots in Python

• [💻] Clustering Methods with SciPy

• [💻] Feature Engineering for NLP in Python

• [💻] Machine Translation in Python

• [💻] Natural Language Processing Fundamentals in Python

• [💻] Natural Language Generation in Python

• [💻] RNN for Language Modeling

• [💻] Regular Expressions in Python

• [💻] Sentiment Analysis in Python

• [💻] Spoken Language Processing in Python

• [ ] Notebook: NNLM - Predict Next Word

• [ ] Notebook: Word2Vec

• [ ] Notebook: FastText Sentence Classification

• [ ] Notebook: TextCNN - Binary Sentiment Classification

• [ ] Notebook: TextRNN - Predict Next Step

• [ ] Notebook: TextLSTM - Autocomplete

• [ ] Notebook: Bi-LSTM - Predict Next Word in Long Sentence

• [ ] Notebook: SeqSeq - Change Word

• [ ] Notebook: Seq2Seq with Attention - Translate

• [ ] Notebook: Bi-LSTM with Attention - Binary Sentiment Classification

• [ ] Notebook: The Transformer - Translate

• [ ] Notebook: The Transformer - Greedy Decoder

• [ ] Notebook: BERT - NSP and MLM

• [ ] Notebook: Logistic regression-Tf-Idf baseline

• [ ] RNN and LSTM

• [ ] Spacy Tutorial

• [ ] Stanford CS224U: Natural Language Understanding | Spring 2019

  • [ ] Lecture 1 – Course Overview | Stanford CS224U: Natural Language Understanding | Spring 2019 1:12:59
  • [ ] Lecture 2 – Word Vectors 1 | Stanford CS224U: Natural Language Understanding | Spring 2019 1:17:10
  • [ ] Lecture 3 – Word Vectors 2 | Stanford CS224U: Natural Language Understanding | Spring 2019 1:16:52
  • [ ] Lecture 4 – Word Vectors 3 | Stanford CS224U: Natural Language Understanding | Spring 2019 0:38:20
  • [ ] Lecture 5 – Sentiment Analysis 1 | Stanford CS224U: Natural Language Understanding | Spring 2019 1:10:44
  • [ ] Lecture 6 – Sentiment Analysis 2 | Stanford CS224U: Natural Language Understanding | Spring 2019 1:03:23
  • [ ] Lecture 7 – Relation Extraction | Stanford CS224U: Natural Language Understanding | Spring 2019 1:19:04
  • [ ] Lecture 8 – NLI 1 | Stanford CS224U: Natural Language Understanding | Spring 2019 1:15:02
  • [ ] Lecture 9 – NLI 2 | Stanford CS224U: Natural Language Understanding | Spring 2019 1:15:35
  • [ ] Lecture 10 – Grounding | Stanford CS224U: Natural Language Understanding | Spring 2019 1:23:15
  • [ ] Lecture 11 – Semantic Parsing | Stanford CS224U: Natural Language Understanding | Spring 2019 1:07:05
  • [ ] Lecture 12 – Evaluation Methods | Stanford CS224U: Natural Language Understanding | Spring 2019 1:18:32
  • [ ] Lecture 13 – Evaluation Metrics | Stanford CS224U: Natural Language Understanding | Spring 2019 1:11:32
  • [ ] Lecture 14 – Contextual Vectors | Stanford CS224U: Natural Language Understanding | Spring 2019 1:14:33
  • [ ] Lecture 15 – Presenting Your Work | Stanford CS224U: Natural Language Understanding | Spring 2019 1:15:11

• [ ] Stanford CS224N: Stanford CS224N: NLP with Deep Learning | Winter 2019

  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 1 – Introduction and Word Vectors 1:21:52
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 2 – Word Vectors and Word Senses 1:20:43
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 3 – Neural Networks 1:18:50
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 4 – Backpropagation 1:22:15
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 5 – Dependency Parsing 1:20:22
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 6 – Language Models and RNNs 1:08:25
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 7 – Vanishing Gradients, Fancy RNNs 1:13:23
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 8 – Translation, Seq2Seq, Attention 1:16:56
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 9 – Practical Tips for Projects 1:22:39
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 10 – Question Answering 1:21:01
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 11 – Convolutional Networks for NLP 1:20:18
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 12 – Subword Models 1:15:30
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 13 – Contextual Word Embeddings 1:20:18
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 14 – Transformers and Self-Attention 0:53:48
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 15 – Natural Language Generation 1:19:37
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 16 – Coreference Resolution 1:19:20
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 17 – Multitask Learning 1:11:54
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 18 – Constituency Parsing, TreeRNNs 1:20:37
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 19 – Bias in AI 0:56:03
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 20 – Future of NLP + Deep Learning 1:19:15
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2020 | Low Resource Machine Translation 1:15:45
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2020 | BERT and Other Pre-trained Language Models 0:54:28

• [ ] Stanford: Natural Language Processing | Dan Jurafsky, Christopher Manning

  • [ ] Course Introduction 0:12:52
  • [ ] Regular Expressions 0:11:25
  • [ ] Regular Expressions in Practical NLP 0:06:05
  • [ ] Word Tokenization 0:14:26
  • [ ] Word Normalization and Stemming 0:11:48
  • [ ] Sentence Segmentation 0:05:34
  • [ ] Defining Minimum Edit Distance 0:07:05
  • [ ] Computing Minimum Edit Distance 0:05:55
  • [ ] Backtrace for Computing Alignments 0:05:56
  • [ ] Weighted Minimum Edit Distance 0:02:48
  • [ ] Minimum Edit Distance in Computational Biology 0:09:30
  • [ ] Introduction to N grams 0:08:41
  • [ ] Estimating N gram Probabilities 0:09:38
  • [ ] Evaluation and Perplexity 0:11:09
  • [ ] Generalization and Zeros 0:05:15
  • [ ] Smoothing Add One 0:06:31
  • [ ] Interpolation 0:10:25
  • [ ] Good Turing Smoothing 0:15:35
  • [ ] Kneser Ney Smoothing 0:08:59
  • [ ] The Spelling Correction Task 0:05:40
  • [ ] The Noisy Channel Model of Spelling 0:19:31
  • [ ] Real Word Spelling Correction 0:09:20
  • [ ] State of the Art Systems 0:07:10
  • [ ] What is Text Classification 0:08:12
  • [ ] Naive Bayes 0:03:20
  • [ ] Formalizing the Naive Bayes Classifier 0:09:29
  • [ ] Naive Bayes Learning 0:05:23
  • [ ] Naive Bayes Relationship to Language Modeling 0:04:36
  • [ ] Multinomial Naive Bayes A Worked Example 0:08:59
  • [ ] Precision, Recall, and the F measure 0:16:17
  • [ ] Text Classification Evaluation 0:07:17
  • [ ] Practical Issues in Text Classification 0:05:57
  • [ ] What is Sentiment Analysis 0:07:18
  • [ ] Sentiment Analysis A baseline algorithm 0:13:27
  • [ ] Sentiment Lexicons 0:08:38
  • [ ] Learning Sentiment Lexicons 0:14:46
  • [ ] Other Sentiment Tasks 0:11:02
  • [ ] Generative vs Discriminative Models 0:07:50
  • [ ] Making features from text for discriminative NLP models 0:18:12
  • [ ] Feature Based Linear Classifiers 0:13:35
  • [ ] Building a Maxent Model The Nuts and Bolts 0:08:05
  • [ ] Generative vs Discriminative models 0:12:10
  • [ ] Maximizing the Likelihood 0:10:30
  • [ ] Introduction to Information Extraction 0:09:19
  • [ ] Evaluation of Named Entity Recognition 0:06:35
  • [ ] Sequence Models for Named Entity Recognition 0:15:06
  • [ ] Maximum Entropy Sequence Models 0:13:02
  • [ ] What is Relation Extraction 0:09:47
  • [ ] Using Patterns to Extract Relations 0:06:17
  • [ ] Supervised Relation Extraction 0:10:51
  • [ ] Semi Supervised and Unsupervised Relation Extraction 0:09:53
  • [ ] The Maximum Entropy Model Presentation 0:12:14
  • [ ] Feature Overlap Feature Interaction 0:12:52
  • [ ] Conditional Maxent Models for Classification 0:04:11
  • [ ] Smoothing Regularization Priors for Maxent Models 0:29:24
  • [ ] An Intro to Parts of Speech and POS Tagging 0:13:19
  • [ ] Some Methods and Results on Sequence Models for POS Tagging 0:13:04
  • [ ] Syntactic Structure Constituency vs Dependency 0:08:46
  • [ ] Empirical Data Driven Approach to Parsing 0:07:11
  • [ ] The Exponential Problem in Parsing 0:14:31
  • [ ] Instructor Chat 0:09:03
  • [ ] CFGs and PCFGs 0:15:30
  • [ ] Grammar Transforms 0:12:06
  • [ ] CKY Parsing 0:23:26
  • [ ] CKY Example 0:21:25
  • [ ] Constituency Parser Evaluation 0:09:46
  • [ ] Lexicalization of PCFGs 0:07:03
  • [ ] Charniak's Model 0:18:24
  • [ ] PCFG Independence Assumptions 0:09:44
  • [ ] The Return of Unlexicalized PCFGs 0:20:53
  • [ ] Latent Variable PCFGs 0:12:08
  • [ ] Dependency Parsing Introduction 0:10:25
  • [ ] Greedy Transition Based Parsing 0:31:05
  • [ ] Dependencies Encode Relational Structure 0:07:21
  • [ ] Introduction to Information Retrieval 0:09:16
  • [ ] Term Document Incidence Matrices 0:08:59
  • [ ] The Inverted Index 0:10:43
  • [ ] Query Processing with the Inverted Index 0:06:44
  • [ ] Phrase Queries and Positional Indexes 0:19:46
  • [ ] Introducing Ranked Retrieval 0:04:27
  • [ ] Scoring with the Jaccard Coefficient 0:05:07
  • [ ] Term Frequency Weighting 0:06:00
  • [ ] Inverse Document Frequency Weighting 0:10:17
  • [ ] TF IDF Weighting 0:03:42
  • [ ] The Vector Space Model 0:16:23
  • [ ] Calculating TF IDF Cosine Scores 0:12:48
  • [ ] Evaluating Search Engines 0:09:03
  • [ ] Word Senses and Word Relations 0:11:50
  • [ ] WordNet and Other Online Thesauri 0:06:23
  • [ ] Word Similarity and Thesaurus Methods 0:16:18
  • [ ] Word Similarity Distributional Similarity I 0:13:15
  • [ ] Word Similarity Distributional Similarity II 0:08:16
  • [ ] What is Question Answering 0:07:29
  • [ ] Answer Types and Query Formulation 0:08:48
  • [ ] Passage Retrieval and Answer Extraction 0:06:38
  • [ ] Using Knowledge in QA 0:04:25
  • [ ] Advanced Answering Complex Questions 0:04:53
  • [ ] Introduction to Summarization 0:04:46
  • [ ] Generating Snippets 0:07:35
  • [ ] Evaluating Summaries ROUGE 0:05:03
  • [ ] Summarizing Multiple Documents 0:10:42
  • [ ] Instructor Chat II 0:05:24

• [ ] TextBlob Tutorial Series

  • [ ] Natural Language Processing Tutorial With TextBlob -Tokens,Translation and Ngrams 0:11:01
  • [ ] NLP Tutorial With TextBlob and Python - Parts of Speech Tagging 0:05:59
  • [ ] NLP Tutorial With TextBlob & Python - Lemmatizating 0:06:32
  • [ ] NLP Tutorial with TextBlob & Python - Sentiment Analysis(Polarity,Subjectivity) 0:06:31
  • [ ] Building a NLP-based Flask App with TextBlob 0:37:30
  • [ ] Natural Language Processing with Polyglot - Installation & Intro 0:12:49

• [📺 ] fast.ai Code-First Intro to Natural Language Processing

  • [ ] What is NLP? (NLP video 1) 0:22:42
  • [ ] Topic Modeling with SVD & NMF (NLP video 2) 1:06:39
  • [ ] Topic Modeling & SVD revisited (NLP video 3) 0:33:05
  • [ ] Sentiment Classification with Naive Bayes (NLP video 4) 0:58:20
  • [ ] Sentiment Classification with Naive Bayes & Logistic Regression, contd. (NLP video 5) 0:51:29
  • [ ] Derivation of Naive Bayes & Numerical Stability (NLP video 6) 0:23:56
  • [ ] Revisiting Naive Bayes, and Regex (NLP video 7) 0:37:33
  • [ ] Intro to Language Modeling (NLP video 8) 0:40:58
  • [ ] Transfer learning (NLP video 9) 1:35:16
  • [ ] ULMFit for non-English Languages (NLP Video 10) 1:49:22
  • [ ] Understanding RNNs (NLP video 11) 0:33:16
  • [ ] Seq2Seq Translation (NLP video 12) 0:59:42
  • [ ] Word embeddings quantify 100 years of gender & ethnic stereotypes-- Nikhil Garg (NLP video 13) 0:47:17
  • [ ] Text generation algorithms (NLP video 14) 0:25:39
  • [ ] Implementing a GRU (NLP video 15) 0:23:13
  • [ ] Algorithmic Bias (NLP video 16) 1:26:17
  • [ ] Introduction to the Transformer (NLP video 17) 0:22:54
  • [ ] The Transformer for language translation (NLP video 18) 0:55:17
  • [ ] What you need to know about Disinformation (NLP video 19) 0:51:21
  • [📰] Zero to Hero with fastai - Beginner
  • [📰] Zero to Hero with fastai - Intermediate

• [ ] NLP Course | For You

  • [ ] Word Embeddings
  • [ ] Text Classification
  • [ ] Language Modeling
  • [ ] Seq2seq and Attention

• [📺 ] BERT Research Series

• [ ] YouTube: Intro to NLP with Spacy

• [ ] Talk: Practical NLP for the Real World

• [ ] YouTube: Level 3 AI Assistant Conference 2020

  • [📺 ] Conversation Analysis Theory in Chatbots | Michael Szul
  • [📺 ] Designing Practical NLP Solutions | Ines Montani
  • [📺 ] Effective Copywriting for Chatbots | Hans Van Dam
  • [📺 ] Distilling BERT | Sam Sucik
  • [📺 ] Transformer Policies that improve Dialogues: A Live Demo by Vincent Warmerdam
  • [📺 ] From Research to Production – Our Process at Rasa | Tanja Bunk
  • [📺 ] Keynote: Perspective on the 5 Levels of Conversational AI | Alan Nichol

• [📺 ] RASA Algorithm Whiteboard

  • [ ] Introducing The Algorithm Whiteboard 0:01:16
  • [ ] Rasa Algorithm Whiteboard - Diet Architecture 1: How it Works 0:23:27
  • [ ] Rasa Algorithm Whiteboard - Diet Architecture 2: Design Decisions 0:15:06
  • [ ] Rasa Algorithm Whiteboard - Diet Architecture 3: Benchmarking 0:22:34
  • [ ] Rasa Algorithm Whiteboard - Embeddings 1: Just Letters 0:13:48
  • [ ] Rasa Algorithm Whiteboard - Embeddings 2: CBOW and Skip Gram 0:19:24
  • [ ] Rasa Algorithm Whiteboard - Embeddings 3: GloVe 0:19:12
  • [ ] Rasa Algorithm Whiteboard - Embeddings 4: Whatlies 0:14:03
  • [ ] Rasa Algorithm Whiteboard - Attention 1: Self Attention 0:14:32
  • [ ] Rasa Algorithm Whiteboard - Attention 2: Keys, Values, Queries 0:12:26
  • [ ] Rasa Algorithm Whiteboard - Attention 3: Multi Head Attention 0:10:55
  • [ ] Rasa Algorithm Whiteboard: Attention 4 - Transformers 0:14:34
  • [ ] Rasa Algorithm Whiteboard - StarSpace 0:11:46
  • [ ] Rasa Algorithm Whiteboard - TED Policy 0:16:10
  • [ ] Rasa Algorithm Whiteboard - TED in Practice 0:14:54
  • [ ] Rasa Algorithm Whiteboard - Response Selection 0:12:07
  • [ ] Rasa Algorithm Whiteboard - Response Selection: Implementation 0:09:25
  • [ ] Rasa Algorithm Whiteboard - Countvectors 0:13:32
  • [ ] Rasa Algorithm Whiteboard - Subword Embeddings 0:11:58
  • [ ] Rasa Algorithm Whiteboard - Implementation of Subword Embeddings 0:10:01
  • [ ] Rasa Algorithm Whiteboard - BytePair Embeddings 0:12:44

• [📺 ] A brief history of the Transformer architecture in NLP

• [📺 ] The Transformer neural network architecture explained. “Attention is all you need” (NLP)

• [📺 ] How does a Transformer architecture combine Vision and Language? ViLBERT - NLP meets Computer Vision

• [📺 ] Strategies for pre-training the BERT-based Transformer architecture – language (and vision)

• [📺 ] Ilya Sutskever - GPT-2

• [📺 ] NLP Masterclass | Modeling Fallacies in NLP

• [📺 ] What is GPT-3? Showcase, possibilities, and implications

• [📺 ] TextAttack: A Framework for Data Augmentation and Adversarial Training in NLP

• [📰] How the Embedding Layers in BERT Were Implemented

• [📺 ] Easy Data Augmentation for Text Classification

• [📺 ] Webinar: Special NLP Session with Hugging Face

• [📺 ] Spacy IRL 2019

  • [ ] Sebastian Ruder: Transfer Learning in Open-Source Natural Language Processing (spaCy IRL 2019) 0:31:24
  • [ ] Giannis Daras: Improving sparse transformer models for efficient self-attention (spaCy IRL 2019) 0:20:13
  • [ ] Peter Baumgartner: Applied NLP: Lessons from the Field (spaCy IRL 2019) 0:18:44
  • [ ] Justina Petraitytė: Lessons learned in helping ship conversational AI assistants (spaCy IRL 2019) 0:23:48
  • [ ] Yoav Goldberg: The missing elements in NLP (spaCy IRL 2019) 0:30:27
  • [ ] Sofie Van Landeghem: Entity linking functionality in spaCy (spaCy IRL 2019) 0:20:08
  • [ ] Guadalupe Romero: Rethinking rule-based lemmatization (spaCy IRL 2019) 0:14:49
  • [ ] Mark Neumann: ScispaCy: A spaCy pipeline & models for scientific & biomedical text (spaCy IRL 2019) 0:18:59
  • [ ] Patrick Harrison: Financial NLP at S&P Global (spaCy IRL 2019) 0:21:42
  • [ ] McKenzie Marshall: NLP in Asset Management (spaCy IRL 2019) 0:20:32
  • [ ] David Dodson: spaCy in the News: Quartz's NLP pipeline (spaCy IRL 2019) 0:20:56
  • [ ] Matthew Honnibal & Ines Montani: spaCy and Explosion: past, present & future (spaCy IRL 2019) 0:54:13

• [📺 ] The Future of Natural Language Processing

• [📺 ] Sentiment Analysis: Key Milestones, Challenges and New Directions

• [📺 ] Simple and Efficient Deep Learning for Natural Language Processing, with Moshe Wasserblat, Intel AI

• [📺 ] Why not solve biological problems with a Transformer? BERTology meets Biology

• [📺 ] Self-attention step-by-step | How to get meaning from text

• [📺 ] Chat Bot with PyTorch

• [📺 ] NLP with Friends Talks

  • [ ] NLP with Friends, Featured Friend: Tom McCoy 0:36:48
  • [ ] NLP with Friends, Featured Friend: Maarten Sap 0:36:11
  • [ ] NLP with Friends, featured friend: Nitika Mathur 1:01:42
  • [ ] NLP with Friends, Featured Friend: Sabrina J Mielke 1:01:28

• [📺 ] Insincere Question Classification with PyTorch

  • [ ] [PART 1] Insincere Question Classification with PyTorch 0:22:34
  • [ ] [PART 2] Insincere Question Classification with PyTorch 0:33:53
  • [ ] [PART 3] Insincere Question Classification with PyTorch 0:23:49
  • [ ] [PART 4] Insincere Question Classification with PyTorch 0:14:25

• [ ] Crash Course: Linguistics

  • [ ] Crash Course Linguistics Preview 0:02:50
  • [ ] What is Linguistics?: Crash Course Linguistics #1 0:11:11

• [📺 ] Recent Advances in Language Pretraining and Generation

• [📺 ] Talks # 3: Lorenzo Ampil - Introduction to T5 for Sentiment Span Extraction

• [📺 ] Frontiers in ML: Learning from Limited Labeled Data: Challenges and Opportunities for NLP

• [📺 ] DeepLearning.ai NLP talk: Chris Manning

• [📺 ] DeepLearning.ai NLP talk: Oren Etzioni

• [📺 ] DeepLearning.ai NLP talk: Quoc Le

• [📺 ] What can MIR learn from transfer learning in NLP?

• [📺 ] The Narrated Transformer Language Model

• [📺 ] spaCy v3.0: Bringing State-of-the-art NLP from Prototype to Production 00:22:40

• [📺 ] Conversational AI with Transformers and Rule-Based Systems 1:53:24

• [ ] Talk: High Performance Natural Language Processing

• [ ] Talk: EmoTag1200: Understanding the Association between Emojis and Emotions

• [📺 ] Research Paper Walkthrough

  • [ ] Simple Unsupervised Keyphrase Extraction using Sentence Embeddings | Research Paper Walkthrough 0:21:23
  • [ ] Leveraging BERT for Extractive Text Summarization on Lectures | Research Paper Walkthrough 0:20:10
  • [ ] Data Augmentation Techniques for Text Classification in NLP | Research Paper Walkthrough 0:14:33
  • [ ] CRIM at SemEval-2018 Task 9: A Hybrid Approach to Hypernym Discovery | Research Paper Walkthrough 0:23:47
  • [ ] Data Augmentation using Pre-trained Transformer Model (BERT, GPT2, etc) | Research Paper Walkthrough 0:17:43
  • [ ] A Supervised Approach to Extractive Summarisation of Scientific Papers | Research Paper Walkthrough 0:19:01
  • [ ] BLEURT: Learning Robust Metrics for Text Generation | Research Paper Walkthrough 0:13:38
  • [ ] DeepWalk: Online Learning of Social Representations |Machine Learning with Graphs | Research Paper Walkthrough 0:17:44
  • [ ] LSBert: A Simple Framework for Lexical Simplification | Research Paper Walkthrough 0:20:27
  • [ ] SpanBERT: Improving Pre-training by Representing and Predicting Spans | Research Paper Walkthrough 0:14:21
  • [ ] Text Summarization of COVID-19 Medical Articles using BERT and GPT-2 | Research Paper Walkthrough 0:21:52
  • [ ] Extractive & Abstractive Summarization with Transformer Language Models | Research Paper Walkthrough 0:16:58
  • [ ] Unsupervised Multi-Document Summarization using Neural Document Model | Research Paper Walkthrough 0:15:11
  • [ ] SummPip: Multi-Document Summarization with Sentence Graph Compression | Research Paper Walkthrough 0:16:54
  • [ ] Combining BERT with Static Word Embedding for Categorizing Social Media | Research Paper Walkthrough 0:13:51
  • [ ] Reformulating Unsupervised Style Transfer as Paraphrase Generation | Research Paper Walkthrough 0:19:41
  • [ ] PEGASUS: Pre-training with Gap-Sentences for Abstractive Summarization | Research Paper Walkthrough 0:15:04
  • [ ] Evaluation of Text Generation: A Survey | Human-Centric Evaluations | Research Paper Walkthrough 0:15:54
  • [ ] TOD-BERT: Pre-trained Transformers for Task-Oriented Dialogue Systems (Research Paper Walkthrough) 0:15:25
  • [ ] TextRank: Bringing Order into Texts (Research Paper Walkthrough) 0:14:34
  • [ ] Node2Vec: Scalable Feature Learning for Networks |Machine Learning with Graphs (Research Paper Walkthrough) 0:14:33
  • [ ] HARP: Hierarchical Representation Learning for Network |Machine Learning with Graphs (Research Paper Walkthrough) 0:15:10
  • [ ] URL2Video: Automatic Video Creation From a Web Page | AI and Creativity (Research Paper Walkthrough) 0:15:21
  • [ ] On Generating Extended Summaries of Long Documents (Research Paper Walkthrough) 0:14:24
  • [ ] Nucleus Sampling: The Curious Case of Neural Text Degeneration (Research Paper Walkthrough) 0:12:48
  • [ ] T5: Exploring Limits of Transfer Learning with Text-to-Text Transformer (Research Paper Walkthrough) 0:12:47
  • [ ] DialoGPT: Generative Training for Conversational Response Generation (Research Paper Walkthrough) 0:13:17
  • [ ] Hierarchical Transformers for Long Document Classification (Research Paper Walkthrough) 0:12:46
  • [ ] Beyond Accuracy: Behavioral Testing of NLP Models with CheckList (Best Paper ACL 2020) 0:14:00

• [ ] NLP Summit 2020

  • [ ] The 2020 Trends for Applied Natural Language Processing | NLP Summit 2020 0:21:10
  • [ ] NLP Industry Survey Analysis: the landscape of natural language use cases in 2020 | NLP Summit 2020 0:20:23
  • [ ] Auto NLP: Pretrain, Tune & Deploy State-of-the-art Models Without Coding 0:19:57
  • [ ] Proof-of-Concept Delight | NLP Summit 2020 0:16:50
  • [ ] Distributed Natural Language Processing Apps for Financial Engineering | NLP Summit 2020 0:34:49
  • [ ] Bleeding Edge Applications of 2020 Transformers | NLP Summit 2020 0:33:34
  • [ ] How Freshworks Freddy AI leverages NLP for Ethics-First Customer Experiences | NLP Summit 2020 0:26:49
  • [ ] NLP for Recruitment Automation: Building a Chatbot from the Job Description | NLP Summit 2020 0:22:31

• [📺 ] Explainability for Natural Language Processing

• [📺 ] Gibberish Detector

• [📺 ] NLP Lecture 7 Constituency Parsing

  • [ ] NLP Lecture 7 - Overview of Constituency Parsing Lecture 0:01:50
  • [ ] NLP Lecture 7 - Introduction to Constituency Parsing 0:10:29
  • [ ] NLP Lecture 7(a) - Context Free Grammar 0:17:03
  • [ ] NLP Lecture 7(b) - Constituency Parsing 0:13:28
  • [ ] NLP Lecture 7(c) - Statistical Constituency Parsing 0:09:38
  • [ ] NLP Lecture 7(d) - Dependency Parsing 0:17:15

• [📺 ] LING 83 Teaching Video: Constituency Parsing

• [📺 ] SpaCy for Digital Humanities with Python Tutorials

  • [ ] Introduction to SpaCy and Cleaning Data (SpaCy and Python Tutorials for DH - 01) 0:06:07
  • [ ] How to Install SpaCy and Models (Spacy and Python Tutorial for DH 02) 0:07:40
  • [ ] How to Separate Sentences in SpaCy (SpaCy and Python Tutorials for DH - 03) 0:08:33
  • [ ] Spacy and Named Entity Recognition NER (Spacy and Python Tutorial for DH 04) 0:08:32
  • [ ] Finding Parts of Speech (SpaCy and Python Tutorial for DH 05) 0:02:55
  • [ ] Extracting Nouns and Noun Chunks (SpaCy and Python Tutorial for DH 06) 0:05:46
  • [ ] Extracting Verbs and Verb Phrases (SpaCy and Python Tutorial for DH 07) 0:08:10
  • [ ] Lemmatization: Finding the Roots of Words (Spacy and Python Tutorial for DH 08) 0:04:52
  • [ ] Data Visualization with DisplaCy (Spacy and Python Tutorial for DH 09) 0:09:13
  • [ ] Customizing DisplaCy Render Data Visualization (Spacy and Python Tutorial for DH 10) 0:08:19
  • [ ] Finding Quotes in Sentences (SpaCy and Python Tutorial for DH 11) 0:08:45
  • [ ] Introduction to Named Entity Recognition (NER for DH 01) 0:16:43
  • [ ] Machine Learning NER with Python and spaCy (NER for DH 03 ) 0:13:36
  • [ ] How to Use spaCy's EntityRuler (Named Entity Recognition for DH 04 | Part 01) 0:36:50
  • [ ] How to Use spaCy to Create an NER training set (Named Entity Recognition for DH 04 | Part 02) 0:10:32
  • [ ] How to Train a spaCy NER model (Named Entity Recognition for DH 04 | Part 03) 0:15:40
  • [ ] Examining a spaCy Model in the Folder (Named Entity Recognition for DH 05) 0:15:06
  • [ ] What are Word Vectors (Named Entity Recognition for DH 06) 0:18:49
  • [ ] How to Generate Custom Word Vectors in Gensim (Named Entity Recognition for DH 07) 0:23:05
  • [ ] How to Load Custom Word Vectors into spaCy Models (Named Entity Recognition for DH 08) 0:10:46
  • [ ] Getting the Data for Custom Labels (Holocaust NER for DH 09.01) 0:11:00
  • [ ] How to Add a Custom NER Pipe in spaCy and a Custom Label (NER for DH 09.02 ) 0:07:49
  • [ ] How to Training Custom Entities into spaCy Models (Named Entity Recognition for DH 09 03) 0:15:29
  • [ ] How to Add and Place Pipes from other Models into a New Model (NER for DH 09 04) 0:12:24
  • [ ] How to Add Custom Functions to spaCy Pipeline (NER for DH 09.05) 0:15:20
  • [ ] Precision vs. Recall and Adding PERSON to Holocaust NER Pipeline (Named Entity Recognition DH 09.06) 0:26:02
  • [ ] Finalizing the Holocaust NER Pipeline (Named Entity Recognition for DH 09.07) 0:14:16
  • [ ] Classical Latin Named Entity Recognition (NER for DH 10.01) 0:55:30
  • [ ] How to Package spaCy Models (Even with Custom Factories) (NER for DH 11) 0:15:31

• [📺 ] Billion-scale Approximate Nearest Neighbor Search

• [📺 ] Data Science - Fuzzy Record Matching

• [📺 ] Minimum Edit Distance Dynamic Programming

• [📺 ] Cheuk Ting Ho - Fuzzy Matching Smart Way of Finding Similar Names Using Fuzzywuzzy

• [📺 ] What's in a Name? Fast Fuzzy String Matching - Seth Verrinder & Kyle Putnam - Midwest.io 2015

• [📺 ] Jiaqi Liu Fuzzy Search Algorithms How and When to Use Them PyCon 2017

• [📺 ] 1 + 1 = 1 or Record Deduplication with Python | Flávio Juvenal @ PyBay2018

• [📺 ] Mike Mull: The Art and Science of Data Matching

• [📺 ] Record linkage: Join for real life by Rhydwyn Mcguire

• [📺 ] Approximate nearest neighbors and vector models, introduction to Annoy

• [📺 ] Librosa Audio and Music Signal Analysis in Python | SciPy 2015 | Brian McFee

• [ ] Video: Recent Advances in LM Pre-training

• [📺 ] Deep Learning (for Audio) with Python

  • [ ] 1- Deep Learning (for Audio) with Python: Course Overview 0:08:02
  • [ ] 2- AI, machine learning and deep learning 0:31:15
  • [ ] 3- Implementing an artificial neuron from scratch 0:19:05
  • [ ] 4- Vector and matrix operations 0:25:51
  • [ ] 5- Computation in neural networks 0:23:19
  • [ ] 6- Implementing a neural network from scratch in Python 0:21:03
  • [ ] 7- Training a neural network: Backward propagation and gradient descent 0:21:41
  • [ ] 8- TRAINING A NEURAL NETWORK: Implementing backpropagation and gradient descent from scratch 1:03:00
  • [ ] 9- How to implement a (simple) neural network with TensorFlow 2 0:24:37
  • [ ] 10 - Understanding audio data for deep learning 0:32:55
  • [ ] 11- Preprocessing audio data for Deep Learning 0:25:05
  • [ ] 12- Music genre classification: Preparing the dataset 0:37:45
  • [ ] 13- Implementing a neural network for music genre classification 0:33:25
  • [ ] 14- SOLVING OVERFITTING in neural networks 0:26:29
  • [ ] 15- Convolutional Neural Networks Explained Easily 0:35:23
  • [ ] 16- How to Implement a CNN for Music Genre Classification 0:49:10
  • [ ] 17- Recurrent Neural Networks Explained Easily 0:28:35
  • [ ] 18- Long Short Term Memory (LSTM) Networks Explained Easily 0:28:08
  • [ ] 19- How to Implement an RNN-LSTM Network for Music Genre Classification 0:14:29

• [📺 ] Advanced Information Retrieval 2021 - TU Wien

  • [ ] AIR - 2021 Course Introduction 0:21:39
  • [ ] Crash Course IR - Fundamentals 0:46:31
  • [ ] Crash Course IR - Evaluation 0:37:15
  • [ ] Crash Course IR - Test Collections 0:51:12
  • [ ] Word Representation Learning 0:42:02
  • [ ] Sequence Modelling with CNNs and RNNs 0:55:04
  • [ ] Transformer and BERT Pre-training 0:47:15
  • [ ] Introduction to Neural Re-Ranking 0:59:20
  • [ ] Transformer Contextualized Re-Ranking 0:49:06
  • [ ] Domain Specific Applications 0:38:32
  • [ ] Dense Retrieval ❤ Knowledge Distillation 0:59:28

• [📺 ] Introduction to Dense Text Representation

  • [ ] Introduction to Dense Text Representations - Part 1 0:12:56
  • [ ] Introduction to Dense Text Representations - Part 2 0:23:13
  • [ ] Introduction to Dense Text Representation - Part 3 0:38:07
  • [ ] Training State-of-the-Art Sentence Embedding Models 0:43:43

• [📺 ] Fine-tuning a large language model without your own supercomputer

• [📺 ] How to build a custom spell checker using python NLP

• [📺 ] Transformers 🤗 to Rule Them All? Under the Hood of the AI Recruiter Chatbot 🤖, with Keisuke Inoue

• [📺 ] Artificial Intelligence and Natural Language Processing in E-Commerce by Katherine Munro | smec

• [📺 ] Abhishek Thakur - Classifying Search Queries Without User Click Data

• [📺 ] Chatbots Revisted | by Abhishek Thakur | Kaggle Days Warsaw

• [📺 ] Abhishek Thakur - Is That a Duplicate Quora Question?

• [📺 ] Design Considerations for building ML-Powered Search Applications - Mark Moyou

• [📺 ] Analyze Customer Feedback in Minutes, Not Months

• [📺 ] NLP in Feedback Analysis - Yue Ning

• [📺 ] Productionizing an unsupervised machine learning model to understand customer feedback

• [📺 ] Extracting topics from reviews using NLP - Dr. Tal Perri

• [📺 ] Bringing innovation to online retail: automating customer service with NLP

• [📺 ] Transform customer service with machine learning (Google Cloud Next '17)

• [📺 ] Real life aspects of opinion sentiment analysis within customer reviews - Dr. Jonathan Yaniv

• [📺 ] Deep Learning Methods for Emotion Detection from Text - Dr. Liron Allerhand

• [📺 ] Learning How to Learn NLP : Developing Introductory Concepts Through Scaffolded Discoveries

• [📺 ] What are Transformer Neural Networks?

• [📺 ] AppliedMachine Learning 2020 - 15 - Working with Text Data 1:27:08

• [📺 ] AppliedMachine Learning 2020 - 16 - Topic models for text data 1:18:34

• [📺 ] AppliedMachine Learning 2020 - 17 - Word vectors and document embeddings 1:03:04

• [📺 ] A Briefish Introduction to Discourse Representation Theory

• [📺 ] HuggingFace Crash Course - Sentiment Analysis, Model Hub, Fine Tuning

• [📺 ] Huggingface Course Part 1

• [📺 ] Should we care about linguistics?

• [📺 ] Transformers From Scratch

  • [ ] How-to Use HuggingFace's Datasets - Transformers From Scratch #1 0:14:21
  • [ ] Build a Custom Transformer Tokenizer - Transformers From Scratch #2 0:14:17
  • [ ] Building MLM Training Input Pipeline - Transformers From Scratch #3 0:23:11
  • [ ] Training and Testing an Italian BERT - Transformers From Scratch #4 0:30:38

---

Be familiar with multi-modal machine learning

• [ ] CMU: MultiModal Machine Learning Fall 2020

  • [ ] Lecture 1.1: Course Introduction
  • [ ] Lecture 1.2: Multimodal applications and datasets
  • [ ] Lecture 2.1: Basic concepts: neural networks
  • [ ] Lecture 2.2: Basic concepts: network optimization
  • [ ] Lecture 3.1: Visual unimodal representations
  • [ ] Lecture 3.2: Language unimodal representations
  • [ ] Lecture 4.1: Multimodal representation learning
  • [ ] Lecture 4.2: Coordinated representations
  • [ ] Lecture 5.1: Multimodal alignment
  • [ ] Lecture 5.2: Alignment and representation
  • [ ] Lecture 7.1: Alignment and translation
  • [ ] Lecture 7.2: Probabilistic graphical models
  • [ ] Lecture 8.1: Discriminative graphical models
  • [ ] Lecture 8.2: Deep Generative Models
  • [ ] Lecture 9.1: Reinforcement learning
  • [ ] Lecture 9.2: Multimodal RL
  • [ ] Lecture 10.1: Fusion and co-learning
  • [ ] Lecture 10.2: New research directions

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Be familiar with Recommendation Systems

• [G] Recommendation Systems

• [ ] Pluralsight: Understanding Algorithms for Recommendation Systems

• [📺 ] Learning to Rank: From Theory to Production - Malvina Josephidou & Diego Ceccarelli, Bloomberg

• [📺 ] Learning "Learning to Rank"

• [📺 ] Learning to rank search results - Byron Voorbach & Jettro Coenradie [DevCon 2018]

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Be able to implement computer vision models

• [📰] Common architectures in convolutional neural networks

• [📰] Convolutional neural networks

• [📰] Densely Connected Convolutional Networks in Tensorflow

• [📰] EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

• [📰] Multimodal Neurons in Artificial Neural Networks

• [📰] Understanding the receptive field of deep convolutional networks

• [📰] Deep learning in medical imaging - 3D medical image segmentation with PyTorch

• [📰] Intuitive Explanation of Skip Connections in Deep Learning

• [📰] Localization and Object Detection with Deep Learning

• [📰] Understanding coordinate systems and DICOM for deep learning medical image analysis

• [📰] The 9 Deep Learning Papers You Need To Know About (Understanding CNNs Part 3)

• [📰] An overview of object detection: one-stage methods

• [📰] An overview of semantic image segmentation

• [📰] Evaluating image segmentation models

• [📰] Semantic Segmentation in the era of Neural Networks

• [📰] DenseNet Architecture Explained with PyTorch Implementation from TorchVision

• [📰] Group Normalization

• [📰] Squeeze and Excitation Networks Explained with PyTorch Implementation

• [📰] What is Focal Loss and when should you use it?

• [📰] Object Detection for Dummies Part 1: Gradient Vector, HOG, and SS

• [📰] Object Detection for Dummies Part 2: CNN, DPM and Overfeat

• [📰] Object Detection for Dummies Part 3: R-CNN Family

• [📰] A Short Introduction to Generative Adversarial Networks

• [📰] Semi-supervised Learning with GANs

• [📰] Human Pose Estimation

• [📰] How to extract Key-Value pairs from Documents using deep learning

• [📰] Building an image search service from scratch

• [📰] Breaking Linear Classifiers on ImageNet

• [📰] Essential Pil (Pillow) Image Tutorial (for Machine Learning People)

• [📰] A Brief History of CNNs in Image Segmentation: From R-CNN to Mask R-CNN

• [📰] YOLO - You only look once (Single shot detectors)

• [📰] NonCompositional

• [📰] Part 1: Deep Representations, a way towards neural style transfer

• [📰] Looking Inside The Blackbox — How To Trick A Neural Network

• [📰] A gentle introduction to OCR

• [📰] ECCV 2020: Some Highlights

• [ ] AWS: Semantic Segmentation Explained

• [📖] Deep Learning for Computer Vision with Python

• [📖] Practical Python and OpenCV

• [ ] Coursera: Convolutional Neural Networks

• [💻] Image Processing in Python

• [G] Machine Learning Practicum: Image Classification

• [ ] Stanford: CS231N Winter 2016

  • [ ] CS231n Winter 2016: Lecture 1: Introduction and Historical Context 1:19:08
  • [ ] CS231n Winter 2016: Lecture 2: Data-driven approach, kNN, Linear Classification 1 0:57:28
  • [ ] CS231n Winter 2016: Lecture 3: Linear Classification 2, Optimization 1:11:23
  • [ ] CS231n Winter 2016: Lecture 4: Backpropagation, Neural Networks 1 1:19:38
  • [ ] CS231n Winter 2016: Lecture 5: Neural Networks Part 2 1:18:37
  • [ ] CS231n Winter 2016: Lecture 6: Neural Networks Part 3 / Intro to ConvNets 1:09:35
  • [ ] CS231n Winter 2016: Lecture 7: Convolutional Neural Networks 1:19:01
  • [ ] CS231n Winter 2016: Lecture 8: Localization and Detection 1:04:57
  • [ ] CS231n Winter 2016: Lecture 9: Visualization, Deep Dream, Neural Style, Adversarial Examples 1:18:20
  • [ ] CS231n Winter 2016: Lecture 10: Recurrent Neural Networks, Image Captioning, LSTM 1:09:54
  • [ ] CS231n Winter 2016: Lecture 11: ConvNets in practice 1:15:03
  • [ ] CS231n Winter 2016: Lecture 12: Deep Learning libraries 1:21:06
  • [ ] CS231n Winter 2016: Lecture 14: Videos and Unsupervised Learning 1:17:36
  • [ ] CS231n Winter 2016: Lecture 13: Segmentation, soft attention, spatial transformers 1:10:59
  • [ ] CS231n Winter 2016: Lecture 15: Invited Talk by Jeff Dean 1:14:49

• [📺 ] Autoencoders - EXPLAINED 0:10:53

• [📺 ] Building an Image Captioner with Neural Networks 0:12:54

• [📺 ] Convolution Neural Networks - EXPLAINED 0:19:20

• [📺 ] Depthwise Separable Convolution - A FASTER CONVOLUTION! 0:12:43

• [📺 ] Generative Adversarial Networks - FUTURISTIC & FUN AI ! 0:14:20

• [📺 ] How Convolution Works

• [📺 ] Mask Region based Convolution Neural Networks - EXPLAINED! 0:09:34

• [📺 ] Sound play with Convolution Neural Networks 0:11:57

• [📺 ] The Evolution of Convolution Neural Networks 0:24:02

• [📺 ] An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (Paper Explained)

• [📺 ] Deep Residual Learning for Image Recognition (Paper Explained)

• [📺 ] Evolution of Face Generation | Evolution of GANs 0:12:23

• [📺 ] ConvNets Scaled Efficiently 0:13:19

• [📺 ] Implementing ResNet from scratch

• [📺 ] DETR: End-to-End Object Detection with Transformers (Paper Explained)

• [📺 ] Unpaired Image-Image Translation using CycleGANs 0:16:22

• [📺 ] Formula Indexing, Search, and Entry in the MathSeer Project 0:52:07

• [📺 ] What's new in computer vision | July Queensland AI 1:21:33

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Be able to model graphs and network data

• [💻] Network Analysis in Python (Part 1)

• [💻] Network Analysis in Python (Part 2)

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Be able to implement models for timeseries and forecasting

• [💻] Machine Learning for Finance in Python

• [💻] Introduction to Time Series Analysis in Python

• [💻] Machine Learning for Time Series Data in Python

• [💻] Intro to Portfolio Risk Management in Python

• [💻] Financial Forecasting in Python

• [💻] Predicting CTR with Machine Learning in Python

• [💻] Intro to Financial Concepts using Python

• [💻] Fraud Detection in Python

• [💻] Forecasting Using ARIMA Models in Python

• [💻] Introduction to Portfolio Analysis in Python

• [💻] Credit Risk Modeling in Python

• [💻] Machine Learning for Marketing in Python

• [🅤]ꭏ Machine Learning for Trading

• [🅤]ꭏ Time Series Forecasting

• [📺 ] AppliedMachine Learning 2020 - 21 - Time Series and Forecasting 1:12:36

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Be familiar with basics of Reinforcement Learning

• [ ] DeepLizard: Reinforcement Learning - Goal Oriented Intelligence

  • [ ] Reinforcement Learning Series Intro - Syllabus Overview 0:05:51
  • [ ] Markov Decision Processes (MDPs) - Structuring a Reinforcement Learning Problem 0:06:34
  • [ ] Expected Return - What Drives a Reinforcement Learning Agent in an MDP 0:06:47
  • [ ] Policies and Value Functions - Good Actions for a Reinforcement Learning Agent 0:06:52
  • [ ] What do Reinforcement Learning Algorithms Learn - Optimal Policies 0:06:21
  • [ ] Q-Learning Explained - A Reinforcement Learning Technique 0:08:37
  • [ ] Exploration vs. Exploitation - Learning the Optimal Reinforcement Learning Policy 0:10:06
  • [ ] OpenAI Gym and Python for Q-learning - Reinforcement Learning Code Project 0:07:52
  • [ ] Train Q-learning Agent with Python - Reinforcement Learning Code Project 0:08:59
  • [ ] Watch Q-learning Agent Play Game with Python - Reinforcement Learning Code Project 0:07:22
  • [ ] Deep Q-Learning - Combining Neural Networks and Reinforcement Learning 0:10:50
  • [ ] Replay Memory Explained - Experience for Deep Q-Network Training 0:06:21
  • [ ] Training a Deep Q-Network - Reinforcement Learning 0:09:07
  • [ ] Training a Deep Q-Network with Fixed Q-targets - Reinforcement Learning 0:07:35
  • [ ] Deep Q-Network Code Project Intro - Reinforcement Learning 0:06:26
  • [ ] Build Deep Q-Network - Reinforcement Learning Code Project 0:16:51
  • [ ] Deep Q-Network Image Processing and Environment Management - Reinforcement Learning Code Project 0:21:53
  • [ ] Deep Q-Network Training Code - Reinforcement Learning Code Project 0:19:46

---

Be able to perform hyperparameter tuning

• [ ] A recipe for training neural networks

• [📰] Hyperparameter tuning for machine learning models

• [📰] Hacker's Guide to Hyperparameter Tuning

• [📰] Environment and Distribution Shift

• [ ] Coursera: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

• [💻] Model Validation in Python

• [💻] Hyperparameter Tuning in Python

• [G] Testing and Debugging

• [ ] Troubleshooting Deep Neural Networks

• [📺 ] How do GPUs speed up Neural Network training? 0:08:19

• [📺 ] Why use GPU with Neural Networks? 0:09:43

• [📺 ] Auto-Tuning Hyperparameters with Optuna and PyTorch

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Be familiar with literature on model interpretability

• [📰] Model Interpretation Frameworks

• [📰] How to leverage Explainable Machine Learning

• [📰] TracIn — A Simple Method to Estimate Training Data Influence

• [📰] How to Explain the Prediction of a Machine Learning Model?

• [ ] NeurIPS 2020: Tutorial on ExplainingMachine Learning Predictions: State-of-the-art, Challenges, and Opportunities

• [📺 ] Jay Alammar - Take A Look Inside Language Models With Ecco

• [📺 ] How do we check if a neural network has learned a specific phenomenon?

• [📺 ] What is Adversarial Machine Learning and what to do about it? – Adversarial example compilation

• [📺 ] SE4AI: Explainability and Interpretability (Part 1) 1:17:45

• [📺 ] SE4AI: Explainability and Interpretability (Part 2) 1:21:50

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Be able to optimize models for inference

• [📰] A Survey of Methods for Model Compression in NLP

• [📰] Why you should convert your NLP pipelines to ONNX

• [📰] Neural Network Pruning

• [📰] FasterAI

• [📰] Is the future of Neural Networks Sparse? An Introduction (1/N)

• [📰] Sparse Neural Networks (2/N): Understanding GPU Performance.

• [📰] Block Sparse Matrices for Smaller and Faster Language Models

• [📰] Plunging Into Model Pruning in Deep Learning

• [📰] How to accelerate and compress neural networks with quantization

• [📰] Scaling up BERT-like model Inference on modern CPU - Part 1

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Be able to write unit tests

• [📰] Effective testing for machine learning systems

• [📰] Unit Testing for Data Scientists

• [📰] Testing in Production, the safe way

• [📰] How to cheat at unit tests with pytest and Black

• [📰] 4 Lesser-Known Yet Awesome Tips for Pytest

• [📰] How to Unit Test Deep Learning: Tests in TensorFlow, mocking and test coverage

• [📰] Test-Driven Machine Learning Development (Deployment Series: Guide 07)

• [💻] Unit Testing for Data Science in Python

• [ ] Pluralsight: Test-driven Development: The Big Picture

• [ ] Test Driven Development with Python

• [ ] Thoughtbot: Fundamentals of TDD

• [🅤]ꭏ Software Analysis & Testing

• [🅤]ꭏ Software Testing

• [🅤]ꭏ Software Debugging

• [📺 ] Beyond Accuracy: Behavioral Testing of NLP Models with CheckList | AISC

• [📺 ] Lecture 10:Machine Learning Testing & Explainability (Full Stack Deep Learning - Spring 2021) 1:41:12

• [📺 ] Lab 8: Testing and Continuous Integration (Full Stack Deep Learning - Spring 2021) 0:13:26

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Be familiar withMachine Learning System Design

• [📰] Architecting a Machine Learning Pipeline

• [📰] Combining rule engines and machine learning

• [📰] Deploying Machine Learning Models: A Checklist

• [📰] Getting machine learning to production

• [📰] How to build scalable Machine Learning systems — Part 1/2

• [📰] How to properly ship and deploy your machine learning model

• [📰] How to put machine learning models into production

• [📰] Key Concepts for Deploying Machine Learning Models to Mobile

• [📰] Machine Learning to Production

• [📰] Machine learning is going real-time

• [📰] Machine Learning Infrastructure Tools for Model Building

• [📰] Machine Learning Infrastructure Tools for Production (Part 1)

• [📰] Machine Learning Infrastructure Tools for Production

• [📰] How to Deploy a Machine Learning Model

• [📰] Building a feature store

• [📰] Model artifacts: the war stories

• [📰] Machine learning system design

• [📰] What isMachine Learning model governance?

• [📰] Building scalable and efficientMachine Learning Pipelines

• [📰] What are common dataset challenges at scale?

• [📰] How to technically distinguish among data projects?

• [📰] SecuringMachine Learning applications

• [📰] Data Pipelines — Agile considerations

• [📰] Data Lineage — An Operational perspective

• [📰] The Ultimate Guide to Model Retraining

• [📖] Machine Learning Systems Design

• [ ] Doc: Lecture 3: Data engineering

• [💻] Data Engineering for Everyone

• [📺 ] AppliedMachine Learning in Production

  • [ ] Objective · AppliedMachine Learning in Production 0:02:38
  • [ ] Solution · AppliedMachine Learning in Production 0:08:53
  • [ ] Evaluation · AppliedMachine Learning in Production 0:04:02
  • [ ] Iteration · AppliedMachine Learning in Production 0:04:35
  • [ ] Annotation · AppliedMachine Learning in Production 0:14:34
  • [ ] Exploratory Data Analysis (EDA) · AppliedMachine Learning in Production 0:09:02

• [📺 ] SE4AI: Software Architecture of AI-Enabled Systems 1:14:24

• [📺 ] SE4AI: Invited Talk Molham Aref "Business Systems with Machine Learning " 0:47:53

• [📺 ] MLOps #4: Shubhi Jain - Building anMachine Learning Platform @SurveyMonkey 0:55:42

• [📺 ] MLOps Meetup #6: Mid-Scale Production Feature Engineering with Dr. Venkata Pingali 1:01:35

• [📺 ] MLOps meetup #5 High StakesMachine Learning with Flavio CLesio 0:55:27

• [📺 ] MLOps meetup #7 Alex Spanos // TrueLayer 's MLOps Pipeline 0:56:17

• [📺 ] #11 Machine Learning at scale in Mercado Libre with Carlos de la Torre 0:59:28

• [📺 ] MLOps #14: Kubeflow vs MLflow with Byron Allen 0:54:57

• [📺 ] MLOps #15 - Scaling Human in the Loop Machine Learning with Robert Munro 0:55:04

• [📺 ] MLOps #18 // Nubank - Running a fintech on ML 0:53:19

• [📺 ] Feature Stores: An essential part of theMachine Learning stack to build great data / Kevin Stumpf - CTO at Tecton 1:05:46

• [📺 ] MLOps #31 Path to Production and Monetizing Machine Learning // Vin Vashishta - Data Scientist 0:56:35

• [📺 ] MLOps #35: Streaming Machine Learning with Apache Kafka and Tiered Storage // Kai Waehner, Confluent 0:52:50

• [📺 ] Luigi in Production // MLOps Coffee Sessions #18 // Luigi PatrunoMachine Learning in Production 0:47:23

• [📺 ] The Current MLOps Landscape // Nathan Benaich & Timothy Chen // MLOps Meetup #43 0:58:31

• [📺 ] MLSys Seminars Fall 2020

  • [ ] Stanford MLSys Seminar Episode 0:Machine Learning + Systems 0:11:49
  • [ ] Stanford MLSys Seminar Episode 1: Marco Tulio Ribeiro 1:00:38
  • [ ] Stanford MLSys Seminar Episode 2: Matei Zaharia 0:59:44
  • [ ] Stanford MLSys Seminar Episode 3: Virginia Smith 1:00:55
  • [ ] Stanford MLSys Seminar Episode 4: Alex Ratner 1:13:34
  • [ ] Stanford MLSys Seminar Episode 5: Chip Huyen 1:06:44

• [📺 ] Xavier Amatriain on Practical Deep Learning Systems (Full Stack Deep Learning - November 2019)

• [📰] Decoding Netflix: Metaflow

• [📺 ] Avoid cascading failures in a distributed system

• [📺 ] How databases scale writes: The power of the log

• [📺 ] How to avoid a single point of failure in distributed systems

• [📺 ] How to start with distributed systems? Beginner's guide to scaling systems.

• [📺 ] What is Distributed Caching? Explained with Redis!

• [📺 ] Why do Databases fail? AntiPatterns to avoid!

• [📺 ] A friendly introduction to System Design

• [📺 ] Designing Instagram: System Design of News Feed

• [📺 ] Introduction to NoSQL databases

• [📺 ] System Design Basics: Horizontal vs. Vertical Scaling

• [📺 ] What is an API and how do you design it?

• [📺 ] Service discovery and heartbeats in micro-services

• [📺 ] System Design: Tinder as a microservice architecture

• [📺 ] What is Load Balancing?

• [📺 ] What is a microservice architecture and it's advantages?

• [📺 ] What is Consistent Hashing and Where is it used?

• [📺 ] What is a Message Queue and Where is it used?

• [📺 ] 5 Tips for System Design Interviews

• [📺 ] Whatsapp System Design: Chat Messaging Systems for Interviews

• [📺 ] Capacity Estimation: How much data does YouTube store daily?

• [📺 ] What is Database Sharding?

• [📺 ] How Netflix onboards new content: Video Processing at scale

• [📺 ] What is the Publisher Subscriber Model?

• [📺 ] Distributed Consensus and Data Replication strategies on the server

• [📺 ] System design : Design Autocomplete or Typeahead Suggestions for Google search

• [📺 ] Relational database index vs. NoSQL index

• [📺 ] What's an Event Driven System?

• [📺 ] Distributed Consensus and Data Replication strategies on the server

• [📺 ] What is an API and how do you design it?

• [📺 ] What is Distributed Caching? Explained with Redis!

• [📺 ] Service discovery and heartbeats in micro-services

• [📺 ] Relational database index vs. NoSQL index

• [📺 ] How Netflix onboards new content: Video Processing at scale

• [📺 ] How to start with distributed systems? Beginner's guide to scaling systems.

• [📺 ] Capacity Estimation: How much data does YouTube store daily?

• [📺 ] How databases scale writes: The power of the log

• [📺 ] System design : Design Autocomplete or Typeahead Suggestions for Google search

• [📺 ] Human-Centric Machine Learning Infrastructure @Netflix

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Be able to serveMachine Learning models

• [📰] The implications of picklingMachine Learning models

• [📰] Deploy a Keras Deep Learning Project to Production with Flask

• [📰] Deploy BERT for Sentiment Analysis as REST API using PyTorch, Transformers by Hugging Face and FastAPI

• [📰] Microservice in Python using FastAPI

• [📰] Selecting gunicorn worker types for different python web applications.

• [📰] Better performance by optimizing Gunicorn config

• [📰] Exponential Backoff And Jitter

• [📰] How to Serve Models

• [📰] MLOps concepts for busy engineers: model serving

• [📰] MLOps concepts for busy engineers: model serving

• [📰] Understanding TensorFlow Serving

• [📰] Serving models using Tensorflow Serving and Docker

• [📰] Batch Inference vs Online Inference

• [📰] Online batching with Spell serving

• [📰] Machine Learning System Design: Real-time processing

• [📰] Machine Learning System Design: Models-as-a-service

• [📰] What Does it Mean to Deploy a Machine Learning Model? (Deployment Series: Guide 01)

• [📰] Software Interfaces for Machine Learning Deployment (Deployment Series: Guide 02)

• [📰] Batch Inference for Machine Learning Deployment (Deployment Series: Guide 03)

• [📰] The Challenges of Online Inference (Deployment Series: Guide 04)

• [📰] Online Inference forMachine Learning Deployment (Deployment Series: Guide 05)

• [📰] Model Registries forMachine Learning Deployment (Deployment Series: Guide 06)

• [ ] Video: You trained a machine learning model. Now what?

• [📰] How Data Leakage Impacts Machine Learning Models

• [📰] 5 Challenges to Running Machine Learning Systems in Production

• [📰] Enabling Machine-Learning-as-a-Service Through Privacy Preserving Machine Learning

• [📰] Shadow mode deployments

• [ ] Cortex Blog

  • [ ] Server-side batching: Scaling inference throughput in machine learning
  • [ ] How we served 1,000 models on GPUs for $0.47
  • [ ] Designing a machine learning platform for both data scientists and engineers
  • [ ] How to serve batch predictions with TensorFlow Serving
  • [ ] How to deploy Transformer models for language tasks
  • [ ] How we scale machine learning model deployment on Kubernetes
  • [ ] Why we built a serverless machine learning platform—instead of using AWS Lambda
  • [ ] Why we don’t deploy machine learning models with Flask
  • [ ] How to deploy machine learning models from a notebook to production
  • [ ] A/B testing machine learning models in production
  • [ ] How to deploy 1,000 models on one CPU with TensorFlow Serving
  • [ ] How to reduce the cost of machine learning inference
  • [ ] Improve NLP inference throughput 40x with ONNX and Hugging Face
  • [ ] How to deploy PyTorch Lightning models to production

• [ ] Django Best Practices

• [🅤]ꭏ Authentication & Authorization: OAuth

• [🅤]ꭏ HTTP & Web Servers

• [🅤]ꭏ Designing RESTful APIs

• [🅤]ꭏ Client-Server Communication

• [📺 ] PyConBY 2020: Sebastian Ramirez - ServeMachine Learning models easily with FastAPI

• [📺 ] FastAPI from the ground up

• [📺 ] Python pydantic Introduction – Give your data classes super powers

• [📺 ] PyData Vancouver meetup: cortex.dev : Serving machine learning models in production

• [📺 ] Lecture 11A: DeployingMachine Learning Models (Full Stack Deep Learning - Spring 2021) 0:53:25

• [📺 ] Hands-on serving models using KFserving // Theofilos Papapanagiotou // MLOps Meetup #40 0:57:40

• [📺 ] Shawn Scully: Production and Beyond: Deploying and Managing Machine Learning Models

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Be able to setup batch inference

• [📰] Celery Execution Pools: What is it all about?

• [📰] Distill: Why do we need Flask, Celery, and Redis? (with McDonalds in Between)

• [📰] Celery: an overview of the architecture and how it works

• [📰] Unit Testing Celery Tasks

• [📰] Testing Celery Chains

• [📰] Task Routing in Celery

• [📰] Dynamic Task Routing in Celery

• [📰] Dockerize a Celery app with Django and RabbitMQ

• [📰] How to call a Celery task from another app

• [📰] Distributed Monte Carlo with Celery chords

• [📰] An incredibly simple no-frills Celery setup

• [📰] 3 Strategies to Customise Celery logging handlers

• [📰] Celery task exceptions and automatic retries

• [📰] Concurrency and Parallelism

• [📰] Celery, docker and the missing startup banner

• [📰] Monitoring a Dockerized Celery Cluster with Flower

• [📰] Quick Guide: Custom Celery Task Logger

• [📰] Celery on Docker: From the Ground up

• [📰] Auto-reload Celery on code changes

---

Be able to build interactive UI for models

• [📰] Build and Deploy a Dashboard with Streamlit

• [📰] New layout options for Streamlit

---

Be able to use Docker for containerization

• [📰] How To Pass Environment Info During Docker Builds

• [📰] Pass Docker Environment Variables During The Image Build

• [📰] Setting Default Docker Environment Variables During Image Build

• [📰] Docker Explained Visually, For Non-Technical Folks

• [📰] Tensorflow in Docker

• [📰] Enough Docker to be Dangerous

• [📰] How Docker Can Help You Become A More Effective Data Scientist

• [📰] Deploying conda environments in (Docker) containers - how to do it right

• [📰] Configuring Gunicorn for Docker

• [📰] How to scale services using Docker Compose

• [📰] A Beginner-Friendly Introduction to Containers, VMs and Docker

• [📰] Smaller Docker images with Conda

• [ ] Pluralsight: Docker and Containers: The Big Picture

• [📰] Docker for Machine Learning – Part I

• [📰] Docker for Machine Learning – Part II

• [📰] Docker for Machine Learning – Part III

• [📰] Using Docker to Generate Machine Learning Predictions in Real Time

• [📰] Connection refused? Docker networking and how it impacts your image

• [📰] Faster or slower: the basics of Docker build caching

• [📰] Where’s your code? Debugging ImportError and ModuleNotFoundErrors in your Docker image

• [📰] A tableau of crimes and misfortunes: the ever-useful docker history

• [📰] Broken by default: why you should avoid most Dockerfile examples

• [📰] A review of the official Dockerfile best practices: good, bad, and insecure

• [📰] The best Docker base image for your Python application (February 2021)

• [📰] A deep dive into the official Docker image for Python

• [📰] Using Alpine can make Python Docker builds 50× slower

• [📰] Building on solid ground: ensuring reproducible Docker builds for Python

• [📰] Installing system packages in Docker with minimal bloat

• [📰] Less capabilities, more security: minimizing privilege escalation in Docker

• [📰] Avoiding insecure images from Docker build caching

• [📰] Build secrets in Docker and Compose, the secure way

• [📰] Security scanners for Python and Docker: from code to dependencies

• [📰] The high cost of slow Docker builds

• [📰] Faster Docker builds with pipenv, poetry, or pip-tools

• [📰] Elegantly activating a virtualenv in a Dockerfile

• [📰] Poetry vs. Docker caching: Fight!

• [📰] Speed up pip downloads in Docker with BuildKit’s new caching

• [📰] Multi-stage builds #1: Smaller images for compiled code

• [📰] Multi-stage builds #2: Python specifics—virtualenv, –user, and other methods

• [📰] Multi-stage builds #3: Why your build is surprisingly slow, and how to speed it up

• [📰] Configuring Gunicorn for Docker

• [📰] Activating a Conda environment in your Dockerfile

• [📰] Shrink your Conda Docker images with conda-pack

• [📰] What’s running in production? Making your Docker images identifiable

• [📰] Your Docker build needs a smoke test

• [📰] Docker BuildKit: faster builds, new features, and now it’s stable

• [📰] Docker vs. Singularity for data processing: UIDs and filesystem access

• [📰] Where’s that log file? Debugging failed Docker builds

• [📰] An Introduction to Kubernetes for Data Scientists

• [📰] How to Use Kubernetes Pods for Machine Learning

• [📰] Kubernetes Jobs for Machine Learning

• [📰] Kubernetes CronJobs for Machine Learning

• [📰] Kubernetes Deployments for Machine Learning

• [📰] Kubernetes Services for Machine Learning

• [ ] “Let’s use Kubernetes!” Now you have 8 problems

• [📰] Kubernetes for Python Developers: Part 1

• [ ] Doc: Environment variables in Compose

• [ ] Pluralsight: Docker and Kubernetes: The Big Picture

• [🅤]ꭏ Scalable Microservices with Kubernetes

• [📺 ] Docker

• [📺 ] Why Your Web Server Should Log to Stdout (Especially with Docker)

---

Be able to use Cloud

• [📰] A deep dive into AWS spot instance interruptions

• [📰] Getting started with large-scale ETL jobs using Dask and AWS EMR

• [💻] Cloud Computing for Everyone

• [ ] Pluralsight: AWS Developer: The Big Picture

• [ ] Pluralsight: AWS Networking Deep Dive: Virtual Private Cloud (VPC)

• [ ] Pluralsight: AWS VPC Operations

• [ ] Pluralsight: Building Applications Using Elastic Beanstalk

• [ ] Udemy: AWS Concepts

• [ ] Udemy: AWS Certified Developer - Associate 2018

• [ ] Whitepaper: Architecting for the Cloud AWS Best Practices

• [ ] Whitepaper: AWS Well-Architected Framework

• [ ] Whitepaper: AWS Security Best Practices

• [ ] Whitepaper: Blue/Green Deployments on AWS

• [ ] Whitepaper: Microservices on AWS

• [ ] Whitepaper: Optimizing Enterprise Economics with Serverless Architectures

• [ ] Whitepaper: Practicing Continuous Integration and Continuous Delivery on AWS

• [ ] Whitepaper: Running Containerized Microservices on AWS

---

Be familiar with serverless architecture

• [ ] Udemy: Serverless Concepts

• [ ] Whitepaper: Serverless Architectures with AWS Lambda

• [📺 ] Deploying a machine learning model to the cloud using AWS Lambda

---

Be able to monitorMachine Learning models

• [📰] Production Machine Learning Monitoring: Outliers, Drift, Explainers & Statistical Performance

• [📰] How to Monitor Models

• [📰] The Playbook to Monitor Your Model’s Performance in Production

• [📰] Monitoring your Machine Learning Model

• [📰] Preventing model drift with continuous monitoring and deployment using Github Actions and Algorithmia Insights

• [📰] Continuous monitoring for data projects

• [📰] Lessons Learned from 15 Years of Monitoring Machine Learning in Production

• [📰] Why is it Important to Monitor Machine Learning Models?

• [📰] Using Statistical Distances for Machine Learning Observability

• [📰] The Model’s Shipped; What Could Possibly go Wrong?

• [📰] Quality assurance in data science

• [📺 ] Instrumentation, Observability & Monitoring of Machine Learning Models

• [📰] Incident Management in Machine Learning Systems

• [📰] Machine Learning Infrastructure Tools —Machine Learning Observability

• [📺 ] MLOps #24 Monitoring theMachine Learning stack // Lina Weichbrodt 0:55:32

• [📺 ] Josh Wills: Visibility and Monitoring for Machine Learning Models

• [📺 ] Lecture 11B: MonitoringMachine Learning Models (Full Stack Deep Learning - Spring 2021) 0:36:55

• [📺 ] OpML '20 - HowMachine Learning Breaks: A Decade of Outages for One LargeMachine Learning Pipeline

• [📺 ] MLOps #28Machine Learning Observability // Aparna Dhinakaran - Chief Product Officer at Arize AI 0:55:04

• [📺 ] MLOps #29 Continuous Evaluation & Model Experimentation // Danny Ma - Founder of Sydney Data Science 1:00:46

• [📺 ] SE4AI: Quality Assessment in Production 1:18:45

• [📺 ] SE4AI: Infrastructure Quality, Deployment and Operations 1:04:54

---

Be able to perform load testing

• [📺 ] Loading Testing with Python

---

Be able to perform A/B testing

• [📰] Multi-Armed Bandit (MAB) – A/B Testing Sans Regret

• [📰] When to Run Bandit Tests Instead of A/B/n Tests

• [📰] A/B Testing Machine Learning Models (Deployment Series: Guide 08)

• [💻] Customer Analytics & A/B Testing in Python

• [🅤]ꭏ A/B Testing

• [🅤]ꭏ A/B Testing for Business Analysts

• [📺 ] Hypothesis testing with Applications in Data Science 0:10:33

---

Be proficient in Python

• [📰] No Really, Python's Pathlib is Great

• [📰] When to switch to Python 3.9

• [📰] A deep dive on Python type hints

• [📰] I wish I knew these things when I learned Python

• [📰] Python Concurrency: The Tricky Bits

• [📰] The Complete Python Development Guide

• [📰] Speeding Up Python with Concurrency, Parallelism, and asyncio

• [📰] Speed Up Your Python Program With Concurrency

• [📰] A Python prompt into a running process: debugging with Manhole

• [ ] Regex For Noobs (like me!) - An Illustrated Guide

• [📖] A Byte of Python

• [📖] Learn Python The Hard way

• [📖] Python 201

• [📖] The Python 3 Standard Library By Example

• [📖] Writing Idiomatic Python 3

• [ ] Calmcode: ray

• [ ] Codecademy: Learn Python

• [ ] Cognitiveclass.ai: Python for Data Science

• [💻] Python for R Users

• [💻] Python for Spreadsheet Users

• [💻] Importing Data in Python (Part 1)

• [💻] Intermediate Python for Data Science

• [💻] Python Data Science Toolbox (Part 1)

• [💻] Python Data Science Toolbox (Part 2)

• [💻] Intro to Python for Finance

• [💻] Writing Efficient Python Code

• [💻] Writing Functions in Python

• [💻] Working with Dates and Times in Python

• [ ] edX: Introduction to Python for Data Science

• [ ] edX: Programming with Python for Data Science

• [ ] Google's Python Class

• [ ] Treehouse: Python Basics

• [ ] TheNewBoston: Python Programming Tutorials

• [📺 ] Python 3 Programming Tutorial - Regular Expressions / Regex with re

• [📺 ] Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

• [📺 ] Python Concurrency and Multithreading

• [📺 ] Aaron Richter- Parallel Processing in Python| PyData Global 2020

• [📺 ] The Clean Architecture in Python

---

Have a general understanding of other parts of the stack

• [📖] Refactoring UI

• [ ] Codecademy: Learn HTML

• [ ] Codecademy: Learn SASS

• [ ] Codecademy: Make a website

• [ ] Codecademy: Learn ReactJS: Part I

• [ ] Codecademy: Learn ReactJS: Part II

• [ ] Codecademy: Learn JavaScript

• [ ] Codecademy: Jquery Track

• [ ] Codecademy: Learn Ruby

• [ ] Code School: Fundamentals of Design

• [ ] Code School: Blasting Off with Bootstrap

• [ ] (ES6) - Beau teaches JavaScript

• [ ] Pluralsight: UX Fundamentals

• [ ] Pluralsight: HTML, CSS, and JavaScript: The Big Picture

• [ ] Pluralsight: CSS Positioning

• [ ] Pluralsight: Introduction to CSS

• [ ] Pluralsight: CSS: Specificity, the Box Model, and Best Practices

• [ ] Pluralsight: CSS: Using Flexbox for Layout

• [ ] Pluralsight: Using The Chrome Developer Tools

• [ ] Thoughtbot: Design for Developers

• [ ] Treehouse: HTML

• [ ] Treehouse: Javascript Booleans

• [🅤]ꭏ ES6 - JavaScript Improved

• [🅤]ꭏ Intro to Javascript

• [🅤]ꭏ Object Oriented JS 1

• [🅤]ꭏ Object Oriented JS 2

• [ ] Udemy: Understanding Typescript

---

Be familiar with fundamental Computer Science concepts

• [📰] Asymptotic Analysis Explained with Pokémon: A Deep Dive into Complexity Analysis

• [📖] Grokking Algorithms

• [ ] Codecademy: Big O

• [ ] Crashcourse: Computer Science

• [ ] Khan Academy: Data Structures

• [🅤]ꭏ Intro to Algorithms

• [🅤]ꭏ Intro to Computer Science

• [🅤]ꭏ Intro to Theoretical Computer Science

• [🅤]ꭏ Programming Languages

• [🅤]ꭏ Networking for Web Developers

---

Be able to apply proper software engineering process

• [ ] Pluralsight: Security Awareness: Basic Concepts and Terminology

• [ ] Pluralsight: Secure Software Development

• [ ] Pluralsight: Clean Architecture: Patterns, Practices, and Principles

• [ ] Thoughtbot: Software Development Process

• [ ] Thoughtbot: Refactoring

• [🅤]ꭏ Design of Computer Programs

• [🅤]ꭏ Product Design

• [🅤]ꭏ Rapid Prototyping

• [🅤]ꭏ Software Development Process

---

Be able to efficiently use a text editor

• [📰] Work remotely with PyCharm, TensorFlow and SSH

• [📰] Python remote debugging with PyCharm, CUDA, and Conda

• [📰] How To Use Visual Studio Code for Remote Development via the Remote-SSH Plugin

• [📰] Docker as Remote Interpreter for PyCharm Professional

• [📺 ] Getting Started with Python in Visual Studio Code

• [📺 ] VSCode Keyboard Shortcuts For Productivity

• [📺 ] Getting Started with Jupyter Notebooks in VS Code

• [📺 ] Notebooks in VS Code Are Getting Revamped!

• [📺 ] Getting Started with PyTorch in VS Code

• [📺 ] What every GitHub user should know about VS Code - GitHub Satellite 2020

• [⚙] and GitHub

• [ ] Visual Studio Code Crash Course

---

Be able to communicate and collaborate well

• [G] Technical Writing

• [📖] Emotional Intelligence

• [📖] How to Win Friends & Influence People

• [📖] Influence: The Psychology of Persuasion

• [📖] Leaders Eat Last: Why Some Teams Pull Together and Others Don't

• [📖] Multipliers: How the Best Leaders Make Everyone Smarter

• [📖] Soft Skills: The software developer's life manual

• [📖] The New One Minute Manager

• [ ] Calmcode: Remote Work

• [📺 ] Building a psychologically safe workplace | Amy Edmondson | TEDxHGSE

---

Be familiar with the hiring pipeline

• [📰] What You Need to Know Before Considering a PhD

• [📰] Advice to aspiring data scientists: start a blog

• [📰] Systems Design Interview Guide

• [📰] A Guide to Cold Emailing

• [📰] Uber Data Science Interview

• [📰] Google Data Science Interview

• [📰] Facebook Data Science Interview

• [📰] Acing the Data Science Interview — Part 1

• [📰] Acing the Data Science Interview — Part 2

• [📰] Amazon Data Science Interview

• [📰] Microsoft Data Science Interview

• [📰] Microsoft Data Science Interview

• [📰] Apple AI Interview Questions — Acing the AI Interview

• [📰] Salesforce Data Science Interview— Acing the AI Interview

• [📰] LinkedIn Data Science Interview

• [📰] Netflix Data Science Interview

• [📰] Walmart Data Science Interview

• [📰] Twitter Data Science Interview

• [📰] Ebay Data Science Interview

• [📰] Zillow Data Science Interview

• [📰] Intel Data Science Interview

• [📰] Adobe Data Science Interview

• [📰] Tesla Data Science Interview

• [📰] IBM Data Science Interview

• [📰] Top Data Science Interview Questions & Answers

• [📰] Top Data Science Interview Questions & Answers — Part 2

• [📰] Top Data Science Interview Questions & Answers — Part 3

• [📰] Data Science Interview Questions and Solutions — Linear and Logistic regression

• [📰] Capital One Data Science Interview

• [📰] Paypal Data Science Interview

• [📰] Airbnb Data Science Interview

• [📰] Spotify Data Science Interview Questions

• [📰] Yelp Data Science Interview

• [📰] Twitch Data Science Interview

• [📰] Oracle Data Science Interview

• [📰] Citrix Data Science Interview

• [📰] Quora Data Science Interview

• [📰] Splunk Data Science Interview

• [📰] JP Morgan Data Science Interview

• [📰] Stripe Data Science Interview Questions

• [📰] Box Data Science Interview

• [📰] Instacart Data Science Interview Questions

• [📰] Square Data Science Interview

• [📰] AmEx Data Science Interview

• [📰] Citibank Data Science Interview

• [📰] Sprint Data Science Interview

• [📰] Dropbox Data Science Interview

• [📰] Booking.com Data Science Interview

• [📰] Lyft Data Science Interview

• [📰] Expedia Data Science Interview

• [📰] Shopify Data Science Interview Questions

• [📰] Goldman Sachs Data Science Interviews

• [📰] Workday Data Science Interviews

• [📰] Acing Data Science Interviews

• [📰] Analysis of Data Science Interview Questions

• [📰] Visa Data Science Interviews

• [📰] Data Science Quiz— Part 1

• [📖] Machine Learning Interviews

• [💻] Preparing for Statistics Interview Questions in Python

• [💻] Practicing Machine Learning Interview Questions in Python

• [💻] Kaggle Competition

• [🅤]ꭏ Optimize your GitHub

• [🅤]ꭏ Strengthen Your LinkedIn Network & Brand

• [🅤]ꭏ Data Science Interview Prep

• [🅤]ꭏ Full-Stack Interview Prep

• [🅤]ꭏ Refresh Your Resume

• [🅤]ꭏ Craft Your Cover Letter

• [🅤]ꭏ Technical Interview

• [📺 ] Panel Discussion: Do I need a PhD to work in ML? (Full Stack Deep Learning - Spring 2021)

• [📺 ] The Importance of Writing in a Tech Career - Eugene Yan

• [📺 ] How to prepare for Machine Learning interviews- Part 1 | Applied AI Course

• [📺 ] How to prepare for Machine Learning interviews- Part 2 | Applied AI Course

• [📺 ] Guest Lecture - Chip Huyen - Machine Learning Interviews - Full Stack Deep Learning

---

Broaden Perspective

• [📖] Atomic Habits

• [📖] Deep Work

• [📖] Outliers: The Story of Success

• [📖] Platform: The Art and Science of Personal Branding

• [📖] Rich Dad Poor Dad

• [📖] The Power of Broke

• [📖] The 10X Rule

• [📖] The Millionaire Fastlane

• [📖] The Subtle Art of Not Giving a F**k

• [ ] Calmcode: Pomodoro

• [📺 ] Why specializing early doesn't always mean career success | David Epstein

• [📺 ] Chamath Palihapitiya, Founder and CEO Social Capital, on Money as an Instrument of Change

• [📺 ] How to Build a Personal Monopoly with Jack Butcher

• [📺 ] How to Use Twitter

• [📺 ] A. Jesse Jiryu Davis - Write an Excellent Programming Blog - PyCon 2016

• [📺 ] The GreatMachine Learning Stagnation (Mark Saroufim and Dr. Mathew Salvaris)

• [📺 ] What Machine Learning Can Teach Us About Life: 7 Lessons - Talk Python Live Stream

• [📺 ] Ross Tuck - Things I Believe Now That I'm Old at Laracon EU 2014

Comments

[NghiaDoan20]

Wonderful, but a lot of resource.
Can you publish a learning flow?
This is a good way to use these resources

[Costosmaya]

I agree with @nghiadoan20, a lot of resources but at the same time is so overwhelming to chosoe where to start and what path to choose, so it could be helpful to have that and use all of these awesome resources.

[Masao Yoshida]

Wonderful but not helpful at all!!!
Too many resources, so confusing.

[saurabh belote]

I don't agree with your opinion

[Chris Nelis]

There are several Web3 clouds I know about. Data security is an obvious concern with such platforms -- you have to give away your information to third parties you cannot trust. The problem can be solved with confidential computing (protecting data-in-use), but it's a relatively new tech and not the easiest to implement.
I don't know many teams going in this direction. If you're interested in Web3 or cloud computing, you should probably check Super Protocol out. The project is still in an early stage of development though, but they recently launched a public testnet with several premade Python solutions (face recognition, speech recognition, and image classification). As far as I know, they are going to allow (and incentivize) uploading your own.
Anyway, here's a Typeform (you can google their website yourself Wink): superprotocol.typeform.com/testnet
They send out invitations a few times a week.

[Dharan]

A proper roadmap can help
Rather than dumping resources

[Cali Hunlax]

But what if I don't want to become a machine that learns things?

[Farrukh]

Excellent and appreciated work...

[saurabh belote]

Great work :)