10 Best PyTorch Books for Beginners and Experts
Master PyTorch, the popular deep learning framework, with the best 10 books for beginners and experts. This comprehensive guide covers a wide range of topics, from the basics of tensors and neural networks to advanced PyTorch techniques. With the right book, you can build powerful AI applications and solve real-world problems.
Master PyTorch, the popular deep learning framework, with 10 top-rated books. Learn the basics, advanced techniques, and real-world applications from experts in the field.
1. Deep Learning with PyTorch By Eli Stevens, Luca Antiga and Thomas Viehmann
It is a beginner level book which deals with the neural network concepts from scratch using PyTorch. It covers all the important aspects of PyTorch from tensors to the torch.nn module. Also, it has entire units dedicated to practical application of neural networks.
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2. Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python
This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch's simple to code framework.
3. Deep Learning for coders with fastai & PyTorch By Jeremy Howard and Sylvain Gugger
The speciality of this book is that it does not require learners to have vast knowledge of maths and machine learning. Co-authored by fastai’s founder himself, this book explains everything in a way that is understandable by complete beginners requiring minimal prerequisites.
4. Programming PyTorch for Deep Learning By Ian Pointer
This is also a beginner level book covering all the neural network topics, building these networks using PyTorch and also deploying our models. Designed to take your Deep Learning skills to the next level, this book introduces the cloud based environments followed by in-depth neural network architecture concerning images, videos, audio etc.
5. PyTorch Pocket Reference: Building and Deploying Deep Learning Models By Joe Papa
It is a reference book that can be used whenever we have any confusion about some syntax or concepts on deep learning. The author has tried to put all the important codes, syntax and concepts in an easy to reference and use form, making it less tedious for developers to code complex models.
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6. The Deep Learning with PyTorch Workshop By Hyatt Saleh
Just like a workshop, starting from the basics this book covers the building blocks of deep learning using PyTorch. It gives a head start to absolute beginners starting from basic concepts to more complex models like CNN and RNN. You will also learn to create new images from some available images.
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7. PyTorch 1.x Reinforcement Learning Cookbook By Yuxi (Hayden) Liu
Reinforcement Learning is used to tackle control and optimisation problems in Artificial Intelligence which is widely used in Robotics. This book covers the concepts of Reinforcement Learning using PyTorch and also introduces some deep learning libraries which proves to be very helpful in practical applications.
8. Natural Language Processing with PyTorch By Brian McMahan and Delip Rao
Natural Language Processing is used in applications such as language translation, sentence completion etc. This book by Delip Rao and Brian McMahan focuses on NLP and its applications, starting from the basics and taking a practical approach in dealing with real-world examples.
9. PyTorch Computer Vision Cookbook By Michael Avendi
This book comprises over 70 steps to build a neural network model using PyTorch in a clear and concise way. It covers common Computer Vision concepts and also some of the trickiest problems in the same. Firstly, it introduces to all the common Computer Vision libraries in PyTorch followed by a deep dive into the Computer Vision applications such as self-driving cars.
10. PyTorch Recipes By Pradeepta Mishra
Starting from tensors this book covers all the basic implementation of PyTorch specially suited for newbies. It covers topics like probability distribution, transformations and computational graphs. The author has also tried to deal with common issues faced by developers. Further, it covers all the neural networks algorithms like RNN, CNN, LSTM etc.
Summary
Depending on your level of expertise and requirement you may choose the book that suits you the best. It is advised to complete one book first to understand the basics of PyTorch and further supplement it with the books extending to broader areas like Computer Vision, Language Processing etc.
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