DEV Community: Vishnubhotla V D V Bharadwaj

Top Data Science competition platforms

Vishnubhotla V D V Bharadwaj — Tue, 07 Sep 2021 10:37:50 +0000

Hello, friends!! How are you? Hope you are doing well! In this blog, I am going to share some of the Top Data Science competition platforms that you should check. Ok then, let's go deep into it.

Kaggle

Kaggle is one of the most popular platforms in the world for Data Science! And there are a lot of competitions on Kaggle that you can enter to sharpen your Data Science skills. These competitions range from identifying wheat using image analysis to predicting lung function decline due to pulmonary fibrosis.

These competitions allow the brightest minds in data science to compete and obtain some solutions to serious problems in the world(while winning a cash prize is just a bonus!). So if you are new to data science competitions, then you should start with Kaggle.

There are some easy competitions you can practice on and then move on to the active world-changing competitions with hefty cash prizes.

International Data Analysis Olympiad (IDAO)

The International Data Analysis Olympiad is a data science competition that is open to everyone, whether they be undergraduate, postgraduate, or Ph.D. students or even company employees or new data scientists.

This competition aims to increase the skills of the professionals in data science so that they can adequately fill the industry demand in this field.

There are two rounds of IDAO. The first round is a data science competition where the participants are provided a labeled training set and asked to predict the test data. The second round focuses more on efficiency and so the participants have to solve the same problem as the first round but with tight restrictions on time and memory.

DrivenData

DrivenData is an online platform that aims to solve some of the biggest social challenges in the world using innovations in data science. consequently, it has various online challenges that last two or three months and various data scientists compete in these challenges to find the best solution for difficult predictive problems that occur in the real world.

Some of the online challenges include detecting hateful content in memes, predicting dengue disease spread, predicting damage to buildings by earthquakes, etc. Science these challenges aim to solve problems and learn more about data science, the code submitted by the winners is released under an open-source license so that everyone can learn and improve.

Topcoder

Topcoder has many different challenges that allow participants to learn more about data science also win prices at the same time! Most of the challenges offer reward money but some are just there to learn and grow.

The most famous challenge is the annual Topcoder open which has various competition tracks based on data science, design, competitive programming, and software development. The most successful candidates are invited for a free one-week trip on the on-site finals, where they can win lots of prizes also learn from each other.

Topcoder also has smaller regional events throughout the year that are not as big as Topcoder Open but still allow different people to participate and improve their skills.

Codalab

Codalab is an open-source platform that has various data science competitions. These competitions do not have a high range of cash prizes, but they still offer participants the opportunity to learn more about data science and create efficient code.

These competitions are a great way to learn more about collaboration and finding solutions with a team as Codalab focuses on the programming and a code-building of data in the competitions.

Currently, the most popular of these include the Liver Tumor segmentation challenge, Microsoft COCO image captioning challenge, MAFAT Radar Challenge, etc.

DataHack

DataHack is a platform provided by Analytics Vidhya that hosts a lot of data science hackathons. You can compete with expert data science and machine learning professionals from all over the world and work on real-life data science problems in these hackathons while learning a lot of new skills.

There are a lot of prizes you can win in these hackathons and some even offer job opportunities at top companies. DataHack also hosts exclusive data science events by Analytics Vidhya where you can interact directly with leaders in the data science and machine learning community.

Iron Viz

IronViz is a visualization competition that is hosted by Tableau. It allows data scientists from all over the world to compete with each other in creating the best data visualization that is beautiful as well as informative. Iron Viz has 2 rounds, namely the Iron Viz Qualifier and the Iron Viz Championship. Everyone can participate in the iron Viz Qualifier which is an online competition based on a qualifier theme.

The only thing that participants need to get started is access to the Tableau Desktop. The free version of the software, Tableau Desktop public edition is also available.

Machine Hack

Machine Hack is an online platform that has various machine learning competitions that you can use to test and practice your ML skills. All the hackathons provide a unique opportunity to compete against data scientists from all over the world and enhance your data science skills.

A big advantage of these hackathons is that companies also scope unique talent from here so top performers may even get job offers! Machine Hack also has an option for practicing your data science and machine learning skills so that you can compete when you are fully prepared.

There is also a community contribution section where more than 10,000 data scientists and machine learning developers share their experiences, tips, tutorials, etc so that other participants can learn from them.

That's the end folks. Hope you enjoyed this. Comment below if I missed any. You can connect with me on Twitter where I daily post a thread on Data Science or Machine Learning.

How to approach any Machine Learning problem

Vishnubhotla V D V Bharadwaj — Wed, 01 Sep 2021 01:57:13 +0000

In this blog. I am going to discuss the 6 step framework, an approach to solve any Machine Learning problem. Let's don't waste any time and dive into the topic.

Step 1: Frame the problem

As a first step, you need to articulate your problem by identifying the type which depends on your business problem.

Type can be anything like Binary classification, Unidimensional regression, Multi-class single-label classification, Multi-class multi-label classification, Multidimensional regression, Clustering(unsupervised), other(translation, parsing, boundary box id, etc..)

Step 2: Get the Data

The next step is to get the data and store it in the right format according to your problem statement.

Analyze your data to check whether you have enough data or not also check the quality of the data.

The quality of the data fundamentally determines if you will be able to solve the problem at all or not.

Step 3: Data Pre-processing

After having the data next step is to analyze it and extract insights to make business decisions.

Also, apply basic data pre-processing operations to bring the data in a go to go format.

Choose the right library.

Step 4: Evaluation Metric

The most important step is to know how to evaluate our results.

We need to choose the right evaluation metric according to the problem we are going to solve.

For example: If we have an imbalance dataset then we usually choose the ROC-AUC metric.

Step 5: Split the Data

In any machine learning problem, we split the data into multiple sets like training, validation, and test.

Stratified splitting is the most used for classification problems and K-Fold for regression problems.

The most important thing to note is whatever operations you apply on the train set must be applied to the validation and test set.

Step 6: Apply ML algorithms

And finally, we will apply ML models to the data. We can't say which models work best it's just hit and trail.

Apply multiple algorithms do hyperparameter tuning, evaluate the results, and choose the best model which gives satisfying results.

Benchmark your solution based on your selected evaluation metric.

That's all from my end folks. Hope you enjoyed this. Connect with me on Twitter, where I post daily about DataScience and Machine Learning.

Start your Kaggle journey today!

Vishnubhotla V D V Bharadwaj — Mon, 23 Aug 2021 02:12:16 +0000

Hello folks. How're you doing? Hope everything is fine with you. And today, I want to publicly commit myself to the 2 Articles 1-week challenge. Every week I will write two articles on Data Science from my past experiences or my learnings of the week.

Ok then, keep all those stuff aside. In this blog, I am going to help you to start your Kaggle journey today.

What is Kaggle?

Kaggle is a platform for Data Science competitions.

Competitive machine learning can be a great way to develop and practice your skills, as well as demonstrate your capabilities.

Why Kaggle?

Because it has well-defined problems with data. Great discussion space to learn from others or you can contribute.

You can build up a portfolio of projects. Kaggle offers its audience a chance to get into the biggest Data Science community in the world.

Here are the 5 steps which should be followed to get more from Kaggle.

1. Pick a Programming Language

Pick one programming language and stick with it.

If you are a complete beginner then pick Python because it's a general-purpose programming language and easy to learn.

Get familiar with Kaggle Notebooks.

2. Learn the basics of EDA

Exploratory analysis is the fisrt step in Data Science because it informs the various decisions/ insights you'll make throughout model training and also you use these insights to make business decisions.

As a python user start with matplotlib or seaborn. As a R user start with dplyr or Ggplot2.

Training your first ML model

Before you get started on Kaggle, take your time to train and practice on a manageable and simpler dataset.

It is better to learn and practice techniques like how to split your data, cross-validation, parameter tuning, using performance metrics, etc...

As a python user start with Scikit-Learn.

Learning competitions

Start with the "getting started" category of the competitions.

Kaggle competitions fall into many categories like Featured, Research, Recruitment, and Getting Started.

Getting Started category is great for beginners and also supported by many community-created tutorials.

Focus on learning not on earning

After getting good handson it's time to progress to 'Featured' competitions.

The key to success, if you are a beginner, is patience and learning from mistakes.

It will take a lot of effort and time to get a good ranking. To avoid getting frustrated and discourage choose your battles wisely.

While prize money is great but is not the main focus, the most valuable benefit is learning skills to prepare you for the real world.

Here are some of my personal tips for you :

a. Set incremental goals

b. Review most voted kernels.

c. Ask questions on the forums.

d. Work solo to develop core skills.

e. Don't worry about low ranks.

That's all for the day peeps. Hope you enjoyed this. Stay tuned for my next article on Thursday. In the meanwhile, you can check my Twitter where I post daily threads on Data Science.

8 Best Apps to Learn Coding

Vishnubhotla V D V Bharadwaj — Thu, 19 Aug 2021 05:48:19 +0000

Hello friends!! How are you doing? Hope everything is fine with you. There are many apps relating to programming in the play store (I really don't know about apple phones). But in this blog, I am going to discuss my favorite apps which help me a lot and also helps you too. But before going into apps, let's have a short introduction on Why Coding?

Why Coding?

Coding powers our digital world. Every website, smartphone, computer programme, calculator, and even microwave relies on code in order to operate. This makes coders the architects and builders of the digital age.

Now, let's see the best available coding apps in the play store.

SoloLearn

It is a community learning platform where students can learn, create, and share programming content with peers around the globe.

Programming Hero

Programming Hero is the selected coding game-based learning app for the #1 programming promoting organization, Code.org. We're included in the Hour of Code. In 2019, Programming Hero won the best Tech code Startup Competition in Silicon Valley, California, USA.

Mimo

With a game-like, science-backed learning experience, Mimo is just about the most fun and effective way to start coding Python, JavaScript, HTML, and more. Mimo creates your personalized learning path of bite-size experiences that fit into your daily routine and keeps you motivated.

Programming Hub

This is one of the fastest-growing mobile Edu-tec initiatives which is a one-stop solution for learning a variety of popular programming languages.

Code Hub

Code Hub is the best way to browse and learn to program through your mother tongue. Now it contains HTML and CSS where other courses are going to be added soon. And the main advantage of this is it is Multilingual.

Grasshopper

It is the best way to learn code for beginners with fun, quick lessons. It also teaches to write real JavaScript. It is a code with a Google program.

Encode

The content in this app is divided into different topics, each containing a good handful of lessons. The simplest topics contain between 4 and 7 lessons while the more complex ones can have up to 14 or 15 lessons.

Codeacademy Go

If you're just started learning code, Codeacademy is a great way to get a sense of what coding is and how it works. It is also a great free way to see if coding might be something that speaks to you as a potential career option before jumping in with both feet.

That's all folks. Hope this blog helps you. Can connect with me on Twitter where I share more useful content like this.

Top GitHub repositories to learn Data Science

Vishnubhotla V D V Bharadwaj — Fri, 13 Aug 2021 07:43:23 +0000

Hello everyone!! It's been so long since I wrote my last blog. Let's keep all those crap aside, I am back again with some interesting blogs for you. In this blog let's discuss top GitHub repositories to learn Data Science.

Before going deep into those repositories let's have some introduction part.

GitHub repositories are like treasure troves of valuable resources that can kickstart your Data Science journey. With the plethora of free resources below, you are well-equipped to learn about data science with your very own curriculum.

Awesome Data Science

This repo is hands down the best collection of resources on Data Science. It covers almost all aspects of learning Data Science, starting with the aspect of motivation which explains the way for Data Science.

Data Scientist Road Map

This repo is inspired by a road map of data science skills by Swami Chandrasekaran. It contains the whole package of what it takes to become a data scientist, from the fundamentals, statistics, and programming to machine learning, data visualization, and data munging.

Data Science best resources

It has all sorts of resources - AI articles, Amazon web service, blogs, books, articles, MOOCs, visualizations, Neural Networks, Cloud Computing, REST API, time series and so much more.

DS - CheatSheets

This is just a part of the image from the Table of Contents, There are a lot more

This repo has interesting cheat sheets on topics such as R, SQL, Data Visualizations and etc.

Data Science Resources

This repo is for those looking to both learn Data Science and plan their career for their future with the given career resources.

Data Science Blogs

This is just a part of the image from the blogs section, There are a lot more

This repo contains a massive list of Data Science blogs for you to learn about everything. The list is ordered alphabetically for easy navigation and the link is right beside the title of the blog.

Free Data Science Books

This is just a part of the image from the Table of Contents, There are a lot more

This repo contains free resources for learning Data Science and Big Data. It starts off with an introduction to what Data Science is, then about Data processing and Data Analysis, Statistics, Machine Learning, and lastly applications of Data Science.

Data Science - Wiki

This is just a part of the image from the Table of Contents, There are a lot more

This repo is the list of DevOps guides, scripts, and tutorials. There is a category for Data Science from beginners to the advanced levels, Python Programming, Linux Tutorials, Git, Code editors, and Machine Learning. These tutorials come in the form of articles, youtube videos, online courses and etc.

That's all folks. Thank you for reading till here.

Read my previous blog about Best Data Science blogs to follow Here.

Feel free to see my previous blogs too.

Connect with me

You can connect with me on Twitter

Useful CSS units

Vishnubhotla V D V Bharadwaj — Wed, 30 Jun 2021 14:14:01 +0000

Generally, there is a lot of confusion in the selection or usage of units in CSS. But, they are things that affect the styling. So, they are the ones which should be handled with utmost care.

When writing CSS there are a lot of options for the units you can use. Here are some main ones you need to be aware of and when to use them.

Pixels are the most popular and well-known CSS unit. They are static and don't scale therefore are best for any value and won't change with screen sizes like borders and fixed-size elements.

.button{
    border-radius: 4px;
    border: 2px solid;
}

Percentage %

Percentage units are also popular and are a good choice for responsive design. However, % units don't always work as expected when setting heights. So, it's recommended to use them for widths only.

.wrapper{
    width: 100%;
    padding: 0 2%;
}
We can use a combination of % and px also


.article{
    max-width: 30%;
    padding: 20px;
}

VH and VW

Viewport height and viewport width units are great for areas that need to take up a specific portion of the screen.

.full_screen{
    width: 100vh;
    height: 100vh;
}
.image{
    width: 45vw;
}

EM and REM

Em and Rem units are calculated based on the font size. It's usually best to stick with Rem units because they are based on the root font size and are more predictable.

Generally, the base font size is 16px. Therefore, 1rem = 16px

.heading{
    font_size: 2rem; /* 32px */
    line-height: 1.4;
}
.subheading{
    font_size: 1.5rem; /* 24px */
    line-height: 1.2;
}

Character units are brilliant for setting widths on text blocks. You can limit how many characters wide you want an element to be and that will be different based on the font size of the text.

.article_text{
    max_width: 70ch;
}
.headline{
    width: 15ch;
    white-space: nowrap;
    overflow: hidden;
    text-overflow: ellipsis;
}

Others

Centimetres(cm), Millimetres(mm), Inches(in), Points(pt), Picas(pc). These are some other units that are technically acceptable in CSS, but they aren't web units so it's better to avoid them. However, they can be useful for print specific styles.

There is no best unit!

Don't pick one unit and try to use only that throughout the project, it won't work well. Instead, evaluate each thing you are trying to style and pick the best unit for the job! This will create a much more versatile and professional design that works across devices.

That's a wrap. Hope you enjoyed this. Connect with me on Twitter where I post some useful content.

Error vs Exception

Vishnubhotla V D V Bharadwaj — Tue, 22 Jun 2021 16:16:31 +0000

One of the most difficult parts of programming languages is to know the difference between Error and Exception. I used to get confused a lot about this. So, to help my mates to overcome this problem, I thought of writing a blog on it. After a long time experimenting, I have decided to write this blog in a table fashion(sounds odd, but easy to understand). Here you go.

What is an Exception?

An exception is an event that occurs during the execution of a program that disrupts the normal flow of instructions during the execution of a program.

What is an Error?

Errors are the mistakes or faults in the program that causes the program to behave unexpectedly.

Types of Exception

Exception: Most languages throw an exception at run-time except java. Java provides two types of exceptions. Let's see them.

Checked Exception: Checked exceptions are known to the compiler and must handle at compile time.

Unchecked Exception: These errors are not checked at runtime.

Types of Error

Compile Time Error/ Syntax Error: Common syntactical mistakes in the code.

Run Time Error: Run time errors that are caused by the environment in which the program is running.

Logical Error: Mistakes in the logical flow of the program.

Here comes the Point of Confusion :

Only confusion is between run-time errors and exceptions.
An exception is something that interrupts the normal flow of the program.
Something exceptional has happened within the program which should be handled to run the program uninterruptedly.
An exception that can be handled even in the compile-time in a language like java while that is not true in the case of run-time error.
Most of the run-time errors caused by the environment in which the application is running.
An error must be resolved. You simply cannot ignore them.

Exception

They are caused by the program itself.
Null reference exception is when the program tries to access the property of the null object.
You can recover from the exceptions by handling them through try-catch classes.
It is recommended to handle exceptions in the program.

Error(Run-Time)

Errors are mostly caused by the environment in which the application is running.
OutOfMemoryError occurs when JVM runs out of memory or StackOverFlow error occurs when stack overflows.
You cannot recover the program from the error. So, you must resolve the error.

Examples of Exception

NullPointerException
ArrayIndexOutOfBoundException
IOException

Example of Errors

OutOfMemoryError

Summary

So, Exception is not a run-time error.
There is a clear difference between errors and exceptions in a language like java.
Different languages interpret error differently.

That's a wrap. Share your thoughts in the comment section. Connect with me on Twitter for more awesome content.

Write better functions in Python!!

Vishnubhotla V D V Bharadwaj — Tue, 15 Jun 2021 00:11:16 +0000

By seeing the title you may get an idea of what we are going to discuss. So, let's waste no time and dive into the topic. After writing functions for nearly three years, I have figured out some Six points to write them effectively. Here I am going to discuss them as crisp as I can.

Keys to a Good Function.!

Sensible naming
Has a single responsibility
Includes a docstring
Returns a value
Is no longer than 50 lines
Is idempotent and, if possible, pure.

Let's go deep into each concept.

Naming!

Prefer full English words to abbreviations and non-universally known acronyms. The only reason one might abbreviate words is to save typing, but every modern editor has autocomplete. So, you'll be typing that full name once.

Single Responsibility

A function should have a single responsibility. That is, it should do one thing and only one thing. One great reason is that if every function only does one thing. There is only one reason ever to change it.

Docstrings

I have recently come to know about this. And from then onwards for every function, I have written, I have included them.

Every function requires a docstring.
Use proper grammar and punctuation. Write in complete sentences.
Begins with a one-sentence summary of what the function does.
Uses prescriptive rather than descriptive language.
Can be used as comments also(added advantage).

Return Values

Functions can(and should) be thought of as little self-contained programs. They take some input in the form of parameters and return some results. Parameters are of course optional. Return values, however, are not optional from a Python internals perspective. Even if you try to create a function that doesn't return a value, you can't. If a function would not return a value, the Python interpreter forces it to return none.

Function Length

The length of the function directly affects readability and thus maintainability. So, keep your functions short. 50 lines is a totally arbitrary number that seemed reasonable to me. Most functions you write will(hopefully) be quite a bit shorter.

Idempotency and Functional Purity

An idempotent function always returns the same value given the same set of arguments, regardless of how many times it is called. The result does not depend on non-local variables, the mutability of arguments, or data from any I/O streams.

Pure functions do not have logging statements or print() calls. They do not make use of databases or internet connections. They don't access or modify non-local variables. And they don't call any other non-pure functions.

That's the end friends. These are the things I learned about functions in my coding journey. Hope you enjoyed it. Connect with me on Twitter for more awesome content.

Top DataScience Blogs to follow in 2021

Vishnubhotla V D V Bharadwaj — Mon, 07 Jun 2021 03:44:09 +0000

DataScience widely known as the future. So, to learn present and future concepts in a clear and detailed manner, blogs are one of the easiest methods. Here are the top blogs in the market which should be a must-follow to mold your career in DataScience.

Towards Data Science

Towards Data Science is a Medium publication brewing with audience-oriented content not just about Data Science, but a multitude of related technologies such as Machine Learning, Programming, Visualization, and Artificial Intelligence, spanning across more than 6000 published articles.

Data Science Central

Data Science Central one of the leading repositories of Data Science content that is regularly updated with the latest trends across domains such as Artificial Intelligence, Machine Learning, Deep Learning, Analytics, Big Data, and much more.

Data Science Reddit

Reddit is world-renowned news and discussion website with hundreds of diverse communities and millions of active users. One such community or subreddit, as Reddit likes to call it, is r/DataScience, which is frequented by over 498 thousand members with an average of over 541 active users at all times.

kDnuggets

KDnuggets is a well-known and prestigious site for gaining information about some of the rapidly growing technologies in the world, including Data Science, Artificial Intelligence, Analytics, Machine Learning, Data Mining, Big Data.

Analytics Vidhya

Analytics Vidhya offers a complete Data Science ecosystem through its four vital pillars, which educate you about the top trends in the industry, solidify your fundamentals via online courses, allow you to engage with other individuals over hackathons, and make you a competitive candidate for the various jobs on the platform.

Data Science | Google News

Google's News platform covers the latest trends and bits of information across a wide range of topics, including general news and specific trends in the industry. Being a repertoire of almost every leading source of information on the internet, Google News offers an equally broad range of latest innovations from some of the reputed Data Science platforms.

DataCamp

DataCamp is an industry-leading online course provider for Data Science. Providing hands-on experience with some of the widely used tools in the Data Science industry, such as Python, R, Scala, Power BI, Excel, Tableau, and many others

IBM Big Data & Analytics hub

The multinational giant powering thousands of successful businesses is a pivotal figure in the numerous innovations in the Data Science and Artificial Intelligence industry. IBM believes in sharing the knowledge with the world and created Big Data and Analytics Hub for Data Science fanatics and interested readers where all the information is readily accessible in a systematic manner.

insideBIGDATA

InsideBIGDATA is one of the popular news platforms that offer regular updates and news about the latest strategies and technologies from the IT world. Covering a wide range of topics across Big Data, Data Science, Deep Learning, and AI. InsideBIGDATA excellently delivers impactful industry perspectives.

Facebook DataScience Blog

The top social media giant Facebook is also a key player in the Data Science industry. The enormous user base of over 2.6 Billion users compelled Facebook to invest in reliable and competent Data Science techniques for an in-depth insight into its users.

That's the end friends. Hope you all liked this. Follow me for more such awesome content. Support me on Twitter where I post some really useful content.

Overwrite Bootstrap with CSS code

Vishnubhotla V D V Bharadwaj — Sat, 29 May 2021 13:15:36 +0000

We all know that Bootstrap makes lives easier. But, there are sometimes where we need to overwrite with custom CSS. To overcome that problem there is a simple and basic trick. In this blog, I am going to share this trick with you.

HTML, the language which follows the order of code perfectly. We can take advantage of this very effectively. If you find that your custom styles are not working, make sure that you change the order of link tags in the header.

CSS styles are applied in the order they are linked in your HTML code.

So, if you have two stylesheets say styles1.css and styles2.css which both target the same element.

styles1.css

body {
background-color: red;
}

styles2.css

body {
background-color: blue;
}

If inside the head section of your HTML code, you list your links as this

<link rel="stylesheet" href="styles1.css">
<link rel="stylesheet" href="styles2.css">

Then, the resulting page will be in Blue color.

But if you listed your links like this:

<link rel="stylesheet" href="styles2.css">
<link rel="stylesheet" href="styles1.css">

The resulting page will be in Red.

Essentially both styles are being applied, but the one that's visible at the end is the one applied last.

So, following that logic, if your custom styles are not overriding the bootstrap styles, all you need to do is move the link to your custom stylesheet to a line after the bootstrap CDN link

<link href="https://stackpath.bootstrapcdn.com/bootstrap/4.1.3/css/bootstrap.min.css" rel="stylesheet">
<link rel="stylesheet" href="css/styles.css">

This means that you first load the default bootstrap styles, then you can overwrite some of those components with your own custom CSS.

Just remember one thing HTML follows the order of code.

That's a wrap friends. Hope this small blog helps you through your web development coding journey. Follow me on Twitter for some awesome content.

Python Open Source projects in GitHub

Vishnubhotla V D V Bharadwaj — Sat, 29 May 2021 12:08:14 +0000

Hello everyone. How is it going? Hope you all doing well. Let's dive into the topic. Nowadays we are hearing a lot of buzz about Open Source. So, let's see the evergreen Python's top Open Source projects in Github. In this blog, I am going to share 10 amazing Open Source projects.

1. Manim (⭐33.9k)

Manim - Mathematical Animation Engine

Manim is an animation engine for explanatory math videos. It is basically used to create precise animations programmatically and runs on Python 3.6 and above. Manim uses Python to generate animations programmatically, which makes it possible to specify exactly how each one should run.

DeepFaceLab (⭐26.5k)

DeepFaceLab is an open-source deep fake system created by iperov for face swapping. It provides an imperative and easy-to-use pipeline for people to use with no comprehensive understanding of the deep learning framework or with model implementation required. This system provides a flexible and loose coupling structure for people who needs to strengthen their own pipeline with other features without writing complicated boilerplate code.

Airflow (⭐21.6k)

Airflow is a platform to programmatically author, schedule, and monitor workflows. The pipelines in Airflow allow for writing code that instantiates pipelines dynamically. to use this platform, you will need Python versions 3.5 and above. It allows users to use Python features to create workflows, monitor, schedule, and manage the workflows using the web app. Anyone with Python knowledge can deploy a workflow. It also has plug-and-play operators that are ready to handle your task on Google Cloud Platform, AWS, Microsoft Azure, and many other services.

GPT-2 (⭐14.6k)

GPT-2 is a large transformer-based language model with 1.5 billion parameters, which is trained with a simple objective to predict the next word, given all of the previous words within some text. GPT-2 generates synthetic text samples in response to the model being primed with arbitrary input. It is a large-scale unsupervised language model which generates coherent paragraphs of text, performs rudimentary reading comprehension and machine translation. It can also perform question answering and summarization. It can generate conditional synthetic text samples of unprecedented quality.

Horovod (⭐11.3k)

Horovod is an open-source distributed deep learning training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. Developed by Uber, the goal of Horovod is to make distributed deep learning fast and easy to use. The primary motivation for this project is to make it easy to take a single-GPU training script and successfully scale it to train across many GPUs in parallel. It is fast and easy to use, achieves 90% scaling efficiency for both Inception V3 and ResNet-101, and 68% scaling efficiency for VGG-16.

ML-Agents (⭐11.1k)

The Unity Machine Learning Agents ToolKit(ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents. The agents can be trained using reinforcement learning, imitation learning, neuroevolution, or other machine learning methods through a simple-to-use Python API. Some of its features include support for multiple environment configurations and training scenarios, flexible Unity SDK, built-in support for imitation learning, among others.

XSStrike (⭐9.3k)

XSStrike is a Cross-Site Scripting detection suite equipped with four handwritten parsers. It is an intelligent payload generator, a powerful fuzzing engine as well as an incredibly fast crawler. The key features of XSStrike include multi-threaded crawling, configurable core, WAF detection, complete HTTP support, and more.

NeuralTalk (⭐5.2k)

NeuralTalk is a Python and Numpy source code for learning Multimodal Recurrent Neural Networks that describe images with sentences. NeuralTalk2 is written in Torch and runs on the GPU. It also supports CNN finetuning, which helps a lot of performance.

Xonsh (⭐4.9k)

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What's new in Flutter 2.2 !?

Vishnubhotla V D V Bharadwaj — Thu, 20 May 2021 12:49:04 +0000

We all know that Google is conducting their event Google I/O 2021 from Tuesday, 18 May to Thursday, 21 May. There are a lot of innovations and updates for the previous versions where are discussed in this event. The coding highlight of the event is the announcement of the Flutter 2.2. It starts with a line Flutter is now the most popular framework for building cross-platform development. And all the newly added features justify the line.

There are a total of seven major things discussed. And see each one of them

Flutter 2.2

Incrementing the version number from 2.0 to 2.2. We all know that 2.0 is just announced 2 months back and here the next latest update. This update mainly focussed on enhancing the quality and productivity of flutter.

Null safety by default

Yes, you heard this right. Now, flutter supports Null safety by default. It shortens the cycle of finding errors. Before null safety, the cycle has four phases Validating changes, edit code, compile the change, navigate state. But now there is no need for navigating the state.

Desktop improvements

This update adds stability to desktop apps. Now, we can easily run our apps on desktop during development and improving tools, widgets. It also supports writing platform adaptive apps.

Dev tools

Added new custom memory timeline events. Easier to understand how memory is allocated. Enabled support for third-party tool extensions. Importing the dart developer package gives access to a lot of cool debugging, profiling, and tracing tools.

Web: Background caching using service workers.

Android: Supports deferred components. Which reduces apk size.

iOS: Tooling to precompile shaders to eliminate the first-jank issue.

Payments and Monetization

Google mobile ads SDK for flutter like incorporating null safety and adding adaptive banners. Launched new payments plugin which supports both google pay and apple pay. The biggest upgrade is, moved the in-app purchase plugin from beta to productive.

Flutter in Production

Who actually uses Flutter? Over 30 teams within Google have chosen to build with flutter. For example, the GooglePay team cut their code millions of lines and saving half of their time just by converting code to flutter. Other than Google there are some tech giants like Toyota, Canonical, Sony, Samsung, Microsoft surface also using Flutter. They also updated adobe XD to flutter plugin. And also they are providing alpha flutter support for Universal Windows Platform(UWP) apps.

Flutter + Google

We can also use other Google APIs and services within flutter and dart apps. For example, Dart on Google Cloud run. It takes zero to containerized dart server in just 10 lines. Because of this, we can run the backend very effectively(less amount of binary). For the front end, they have teamed up with firebase to create special i/o an open-source flutter web app powered by firebase services. Check this out here.

That's the end. These are the main topics that are discussed in the Google I/O 2021.

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