What Deep Learning Books Cover TensorFlow And PyTorch?

I’ve been diving into deep learning for a while now, and two books really stand out for TensorFlow and PyTorch. 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow' by Aurélien Géron is a fantastic resource. It starts with the basics and gradually moves to advanced topics, making it perfect for beginners and intermediates. The TensorFlow sections are particularly well-explained with practical examples. For PyTorch, 'Deep Learning with PyTorch' by Eli Stevens, Luca Antiga, and Thomas Viehmann is my go-to. It’s written by PyTorch core developers, so the insights are top-notch. The book balances theory and practice beautifully, with clear code snippets and real-world applications. Both books avoid overwhelming jargon and focus on hands-on learning, which I appreciate.

I’m a hands-on learner, so I prefer books with lots of code and projects. For TensorFlow, 'TensorFlow 2.x in Action' by Thushan Ganegedara is brilliant. It skips the fluff and jumps straight into building models, from CNNs to transformers. The exercises are challenging but rewarding. On the PyTorch side, 'PyTorch Pocket Reference' by Joe Papa is my little bible. It’s not a traditional textbook but a quick-reference guide packed with tips for debugging and optimization. I keep it open whenever I code.

For a deeper dive, 'Learning PyTorch 2.0' by Matthew Rosch is newer and covers torch.compile and other latest features. It’s project-heavy, which keeps things engaging. If you want a mix of both frameworks, 'Deep Learning with TensorFlow and PyTorch' by Antonio Gulli et al. compares them side by side. The contrast helps you choose the right tool for each task. These books are practical, avoiding excessive theory—perfect if you learn by doing.

I’ve found a few gems for TensorFlow and PyTorch. 'Deep Learning with Python' by François Chollet, the creator of Keras, is a must-read. It covers TensorFlow indirectly through Keras but is incredibly intuitive for building neural networks. The second edition even includes newer TensorFlow features. For PyTorch, I adore 'Programming PyTorch for Deep Learning' by Ian Pointer. It’s concise yet packed with actionable advice, from tensors to deploying models. The book’s structure is logical, progressing from fundamentals to advanced techniques like custom layers and mixed precision training.

Another standout is 'Python Deep Learning' by Ivan Vasilev and Daniel Slater. It explores both frameworks in later chapters, comparing their strengths. The PyTorch section dives into dynamic computation graphs, while TensorFlow’s static approach is contrasted neatly. If you’re into research, 'Deep Learning for Coders with Fastai and PyTorch' by Jeremy Howard and Sylvain Gugger is revolutionary. Fastai simplifies PyTorch, making cutting-edge techniques accessible. Each book has a unique angle, whether it’s industry-focused or research-oriented, so pick based on your goals.