9 Best PyTorch Courses - [OCT 2024]

PyTorch is one of the most popular open-source deep learning libraries, developed by Facebook's Artificial Intelligence Research Group (FAIR). It has revolutionized how researchers and developers approach machine learning, particularly in areas such as Natural Language Processing (NLP), Computer Vision, and Artificial Intelligence (AI). Its dynamic computational graph and intuitive interface make it a preferred tool for building and deploying complex neural networks like RNNs, CNNs, and LSTMs.

Unlike static frameworks like TensorFlow, PyTorch operates dynamically, which allows developers to build, modify, and iterate models more easily. This feature has made it the go-to choice for researchers pushing the boundaries of AI research. PyTorch's simplicity, combined with its powerful performance, makes it ideal not only for academic research but also for industry applications, from AI startups to tech giants like Tesla and Uber.

What Are the Key Features of PyTorch?

PyTorch comes with several key features that have solidified its place as a leading deep-learning framework:

• Dynamic Computation Graphs: Modify your model in real time, making experimentation and debugging easier.
• Autograd: Automatic differentiation for building and training neural networks.
• TorchScript: Transition from eager mode to graph mode seamlessly, enabling production-ready model deployment.
• Distributed Training: Easily scale your models across multiple GPUs or even distributed systems for faster training.

These features make PyTorch an excellent tool for both researchers and developers working on advanced deep-learning tasks.

What Makes PyTorch Different from Other Deep Learning Libraries?

PyTorch stands out from other deep learning libraries due to its dynamic computation graph. Unlike frameworks such as TensorFlow that use static graphs, PyTorch allows developers to modify their neural networks during runtime, offering a more flexible and intuitive approach. This dynamic graph structure has made PyTorch the preferred choice for researchers and developers working on complex models like CNNs, RNNs, and LSTMs. Its seamless integration with Python also makes it easier for beginners and professionals alike to get started.

How Do I Get Started with PyTorch?

If you're looking to dive into PyTorch and want to understand the framework from scratch, several online PyTorch courses offer structured learning paths. To help you kickstart your journey, we at Coursesity have curated a list of the Best PyTorch Courses with certificates. These courses cover everything from setting up your environment to building advanced neural networks, ensuring you have the practical skills to excel in AI development.

Best PyTorch Courses List

1. PyTorch: Deep Learning and Artificial Intelligence

2. Deep Neural Networks with PyTorch

3. PyTorch for Deep Learning with Python Bootcamp

4. PyTorch Essential Training: Deep Learning Online Class

5. Modern Reinforcement Learning: Deep Q Learning in PyTorch

6. Transfer Learning for Images Using PyTorch: Essential Training Online Class

7. Foundations of PyTorch

8. Building Your First PyTorch Solution

9. Intro to Deep Learning with PyTorch

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1. PyTorch: Deep Learning and Artificial Intelligence

Ever wondered how AI technologies like OpenAIChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion work? In this course, you will learn the foundations of these groundbreaking applications.

The Pytorch tutorial includes:

• Artificial Neural Networks (ANNs) / Deep Neural Networks (DNNs).
• Predict Stock Returns
• Time Series Forecasting
• Computer Vision
• Building a Deep Reinforcement Learning Stock Trading Bot.
• GANs (Generative Adversarial Networks).
• Recommender Systems
• Image Recognition
• Convolutional Neural Networks (CNNs).
• Recurrent Neural Networks (RNNs).
• Natural Language Processing (NLP) with Deep Learning.
• Demonstrate Moore's Law using Code.
• Transfer Learning to create state-of-the-art image classifiers.
• Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion.

Initially, you will learn some very basic machine learning models and advance towards state-of-the-art concepts.

Along the way, you will learn about all of the major deep learning architectures, such as Deep Neural Networks, Convolutional Neural Networks (image processing), and Recurrent Neural Networks (sequence data).

Finally, you will learn about how to convert your previous code so that it uses PyTorch, and also perform projects such as time series forecasting and how to do stock predictions.

This Pytorch course focuses more on the PyTorch library, rather than deriving any mathematical equations.

You can take PyTorch: Deep Learning and Artificial Intelligence certification course on Udemy.

• Course rating: 4.8 out of 5.0
• Duration: 24.5 hours
• Certificate: Certificate on completion

2. 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.

In this Pytorch course, you will learn how to:

• develop deep learning models using PyTorch.
• start with PyTorch's tensors and Automatic differentiation package.
• use 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

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

Initially, you will learn about Linear Regression and logistic/softmax regression. Followed by Feedforward deep neural networks, the role of different activation functions, normalization, and dropout layers.

Finally, Convolutional Neural Networks and Transfer learning will also be covered in this PyTorch course.

You can take Deep Neural Networks with PyTorch certification course on Coursera.

• Course rating: 4.4 out of 5.0
• Duration: 31 hours
• Certificate: Certificate on purchase

3. 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!

In this Pytorch course, you will learn how to:

• Learn how to use NumPy to format data into arrays.
• Use pandas for data manipulation and cleaning.
• Learn classic machine learning theory principles.
• Use PyTorch Deep Learning Library for image classification.
• Use PyTorch with Recurrent Neural Networks for Sequence Time Series Data.
• Create state-of-the-art Deep Learning models to work with tabular data.

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.

You can take PyTorch for Deep Learning with Python Bootcamp certification course on Udemy.

• Course rating: 4.5 out of 5.0
• Duration: 17 hours
• Certificate: Certificate upon completion

4. 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.

This Pytorch tutorial includes:

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

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

You can take the PyTorch Essential Training: Deep Learning certification course on LinkedIn Learning.

• Course rating: 4.6 out of 5.0
• Duration: 1 hour 21 minutes
• Certificate: Certificate on completion

5. Modern Reinforcement Learning: Deep Q Learning in PyTorch

Turn Deep Reinforcement Learning Research papers into agents that beat classic Atari games.

In this Pytorch course, you will learn how to:

• How to read and implement deep reinforcement learning papers.
• How to code Deep Q learning agents.
• How to Code Double Deep Q Learning Agents.
• How to Code Dueling Deep Q and Dueling Double Deep Q Learning Agents.
• How to write modular and extensible deep reinforcement learning software.
• How to automate hyperparameter tuning with command line arguments.

In this PyTorch tutorial, you will learn a repeatable framework for reading and implementing deep reinforcement learning research papers. You will read the original papers that introduced Deep Q learning, Double Deep Q learning, and Dueling Deep Q learning algorithms.

Plus, you will learn how to implement these in pythonic and concise PyTorch code, which can be extended to include any future deep Q learning algorithms. These algorithms will be used to solve a variety of environments from the Open AI gym's Atari library, including Pong, Breakout, and Bank Heist.

Finally, you will learn the key to making these Deep Q Learning algorithms work, which is how to modify the Open AI Gym's Atari library to meet the specifications of the original Deep Q Learning papers.

You can take the Modern Reinforcement Learning: Deep Q Learning in PyTorch certification course on Udemy.

• Course rating: 4.5 out of 5.0
• Duration: 7 hours
• Certificate: Certificate on completion

6. Transfer Learning for Images Using PyTorch: Essential Training

Discover how to implement transfer learning using PyTorch, the popular machine learning framework.

The Pytorch course includes:

• What Is Transfer Learning?
• Transfer Learning: Fixed Feature Extractor
• Fine-Tuning the ConvNet
• Further Techniques

This course shows you how to leverage PyTorch for a similarly buzzworthy technique i.e. transfer learning. Learn how to implement transfer learning for images using PyTorch, including how to create a fixed feature extractor and freeze neural network layers. Plus, find out about using learning rates and differential learning rates.

You can take the Transfer Learning for Images Using PyTorch: Essential Training certification course on LinkedIn Learning.

• Course rating: 4.7 out of 5.0
• Duration: 1 hour
• Certificate: Certificate on completion

7. Foundations of PyTorch

This Pytorch tutorial covers many aspects of building deep learning models in PyTorch, including neurons and neural networks, and how PyTorch uses differential calculus to train such models and create dynamic computation graphs in deep learning.

The course includes:

• Getting Started with PyTorch for Machine Learning
• Working with Tensors in PyTorch
• Working with Gradients Using the Autograd Library
• Building Dynamic Computation Graphs

In this Pytorch course, you will gain the ability to leverage PyTorch support for dynamic computation graphs and contrast that with other popular frameworks such as TensorFlow.

First, you will learn the internals of neurons and neural networks, and see how activation functions, affine transformations, and layers come together inside a deep learning model.

Next, you will discover how such a model is trained, that is, how the best values of model parameters are estimated. You will then see how gradient descent optimization is smartly implemented to optimize this process.

Plus, you will understand the different types of differentiation that could be used in this process, and how PyTorch uses Autograd to implement reverse-mode auto-differentiation. You will also work with different PyTorch constructs such as Tensors, Variables, and Gradients.

Finally, you will explore how to build dynamic computation graphs in PyTorch. You will round out the course by contrasting this with the approaches used in TensorFlow, another leading deep learning framework that previously offered only static computation graphs, but has recently added support for dynamic computation graphs.

You can take the Foundations of PyTorch certification course on Pluralsight.

• Course rating: 4.5 out of 5.0
• Duration: 3 hours
• Certificate: Certificate on completion

8. Building Your First PyTorch Solution

This Pytorch course covers the important practical aspects of installing PyTorch from scratch on a variety of different platforms and getting going with classification and regression models.

The course includes:

• Installing PyTorch on a Local Machine
• Understanding Linear Regression with a Single Neuron
• Building a Regression Model Using PyTorch
• Building a Classification Model Using PyTorch

In this Pytorch course, Building Your First PyTorch Solution, you will learn how to install PyTorch using pip and conda, and see how to leverage GPU support. Next, you will discover how to hand-craft a linear regression model using a single neuron, by defining the loss function yourself.

Moreover, you will then see how PyTorch optimizers can be used to make this process a lot more seamless. You will understand how different activation functions and dropouts can be added to PyTorch neural networks. Finally, you will explore how to build classification models in PyTorch.

You can take the Building Your First PyTorch Solution certification course on Pluralsight.

• Course rating: 4.0 out of 5.0
• Duration: 2 hours 25 minutes
• Certificate: Certificate on completion

9. Intro to Deep Learning with PyTorch

Learn the basics of deep learning and implement your deep neural networks with this Free PyTorch Course.

In this Pytorch 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.

The course includes:

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

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

You can take the Intro to Deep Learning with PyTorch certification course on Udacity.

FAQs

1. What is PyTorch used for?

PyTorch is primarily used for building deep learning models, including neural networks for tasks such as image recognition, natural language processing, and reinforcement learning.

2. Is PyTorch better than TensorFlow?

Both PyTorch and TensorFlow are powerful frameworks, but PyTorch is often preferred by researchers due to its dynamic computation graph, which allows for more flexibility during model building. TensorFlow, however, is more widely used in production environments.

3. How long does it take to learn PyTorch?

The time it takes to learn PyTorch depends on your familiarity with Python and machine learning concepts. Beginners can expect to spend a few weeks grasping the basics, while more advanced learners may take a couple of months to master it.

4. Can I use PyTorch for production?

Yes, PyTorch has matured into a framework suitable for both research and production. With tools like TorchScript and Caffe2, PyTorch models can be easily deployed in production environments.

5. What is the difference between PyTorch and TensorFlow?

The primary difference lies in the computation graph. PyTorch uses a dynamic computation graph, which makes debugging and experimentation more intuitive, whereas TensorFlow uses a static graph, which can be more efficient in production but harder to modify during development.

Hey! We hope you have found this Online PyTorch Courses list helpful and intriguing. Since you've made it this far then certainly you are willing to learn more and here at Coursesity, it is our duty to enlighten people with knowledge on topics they are willing to learn.

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