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Best PyTorch Courses Online in 2021

akshit161 profile image Akshit Tiwari ・5 min read

PyTorch is an open-source profound learning stage that gives a consistent way from research prototyping to creation organization. It is quickly becoming the most mainstream profound learning framework for Python.

Deep integration into Python permits well-known libraries and bundles to be utilized for effectively composing neural network layers in Python. A rich biological system of instruments and libraries expands PyTorch and supports advancement in PC vision, NLP, and that's just the beginning.

PyTorch has quickly gotten quite possibly the most extraordinary frameworks in the field of Deep Learning. Since its delivery, PyTorch has totally changed the scene in the field of profound learning because of its adaptability, and that it is so natural to utilize when assembling Deep Learning models.

From the above-mentioned points, you will come to realize that learning Pytorch can open another entry of changes in your profession, and you are prepared for it! In this way, we have curated a rundown of the Best PyTorch Courses Online that you can take to learn and get a decent encounter.

1. PyTorch for Deep Learning with Python Bootcamp

Learn how to create state of the art neural networks for deep learning with Facebook's PyTorch Deep Learning library!

Course rating: 4.6 out of 5.0 ( 1,861 Ratings total)

In this course, you will learn how to:

  • use NumPy to format data into arrays.
  • use pandas for data manipulation and cleaning.
  • understand classic machine learning theory principals.
  • use PyTorch Deep Learning Library for image classification.
  • use PyTorch with Recurrent Neural Networks for Sequence Time Series Data.
  • create a state of the art Deep Learning models to work with tabular data.

The course includes:

  • NumPy
  • Pandas
  • Machine Learning Theory
  • Test/Train/Validation Data Splits
  • Model Evaluation - Regression and Classification Tasks
  • Unsupervised Learning Tasks
  • Tensors with PyTorch
  • Neural Network Theory
  • Artificial Neural Networks
  • Convolutional Neural Networks
  • Recurrent Neural Networks

This course focuses on balancing important theory concepts with practical hands-on exercises and projects that let you learn how to apply the concepts in the course to your own data sets!

By the end of this course, you will be able to create a wide variety of deep learning models to solve your own problems with your own data sets.

2. PyTorch Essential Training: Deep Learning

Explore the basics of deep learning using PyTorch. Learn about the components of an image recognition model using the Fashion MNIST dataset.

Course rating: 9,315 total enrollments

The course includes:

  • Fashion MNIST and Neural Networks
  • Working with Classes and Tensors
  • Working with Loss, Autograd, and Optimizers
  • Troubleshooting and CPU/GPU Usage

This course dives into the basics of deep learning using PyTorch. Starting with a working image recognition model, it shows how the different components fit and work in tandem—from tensors, loss functions, and autograd all the way to troubleshooting a PyTorch network.

3. Deep Neural Networks with PyTorch

Learn Deep Neural Networks with PyTorch from IBM. The course will teach you how to develop deep learning models using Pytorch.

Course rating: 4.4 out of 5.0 ( 796 Ratings total)

In this course, you will learn how to:

  • understand and apply your knowledge of Deep Neural Networks and related machine learning methods.
  • use Python libraries such as PyTorch for Deep Learning applications.
  • build Deep Neural Networks using PyTorch.

The course includes:

  • Tensor and Datasets
  • Linear Regression
  • Linear Regression PyTorch Way
  • Multiple Input Output Linear Regression
  • Logistic Regression for Classification
  • Softmax Regression
  • Shallow Neural Networks
  • Deep Networks
  • Convolutional Neural Network
  • Peer Review

The course will teach you how to develop deep learning models using Pytorch and you will start with Pytorch's tensors and Automatic differentiation package.

Then each section will cover different models starting off with fundamentals such as Linear Regression, and logistic/softmax regression. Followed by Feedforward deep neural networks, the role of different activation functions, normalization, and dropout layers.

4. Intro to Deep Learning with PyTorch

Learn the basics of deep learning and implement your own deep neural networks with PyTorch.

In this course, you will learn how to:

  • understand the basic concepts of deep learning such as neural networks and gradient descent.
  • implement a neural network in NumPy and train it using gradient descent with in-class programming exercises.
  • build a neural network to predict student admissions.
  • build your first neural network with PyTorch to classify images of clothing
  • work through a set of Jupyter Notebooks to learn the major components of PyTorch.
  • load a pre-trained neural network to build a state-of-the-art image classifier.
  • use PyTorch to build Convolutional Neural Networks for state-of-the-art computer vision applications.
  • train a convolutional network to classify dog breeds from images of dogs.
  • use a pre-trained convolutional network to create new art by merging the style of one image with the content of another image.
  • implement the paper "A Neural Algorithm of Artistic Style".
  • build recurrent neural networks with PyTorch that can learn from sequential data such as natural language.
  • implement a network that learns from Tolstoy’s Anna Karenina to generate new text based on the novel.
  • use PyTorch to implement a recurrent neural network that can classify text.
  • use your network to predict the sentiment of movie reviews.
  • deploy deep learning models with PyTorch.
  • build a chatbot and compile the network for deployment in a production environment.

The course includes:

  • Introduction to Deep Learning
  • Introduction to PyTorch
  • Deep Learning with PyTorch
  • Convolutional Neural Networks
  • Style Transfer
  • Recurrent Neural Networks
  • Natural Language Classification
  • Deploying with PyTorch

In this course, you will learn the basics of deep learning, and build your own deep neural networks using PyTorch. You will get practical experience with PyTorch through coding exercises and projects implementing state-of-the-art AI applications such as style transfer and text generation.

5. PyTorch for Deep Learning and Computer Vision

Build Highly Sophisticated Deep Learning and Computer Vision Applications with PyTorch.

Course rating: 4.6 out of 5.0 ( 1,331 Ratings total)

In this course, you will learn how to:

  • implement Machine and Deep Learning applications with PyTorch.
  • build Neural Networks from scratch.
  • build complex models through the applied theme of Advanced Imagery and Computer Vision.
  • solve complex problems in Computer Vision by harnessing highly sophisticated pre-trained models.
  • use style transfer to build sophisticated AI applications.
  • work with the tensor data structure.
  • solve complex problems in Computer Vision by harnessing highly sophisticated pre-trained models.
  • use style transfer to build sophisticated AI applications that are able to seamlessly recompose images in the style of other images.

By the end of the course, you will have built state-of-the-art Deep Learning and Computer Vision applications with PyTorch.

Want to learn more? We have a collection of Best PyTorch Courses Online!

*Disclosure: This post includes affiliate links; our team may receive compensation if you purchase products or services from the different links provided in this article.

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