Mastering PyTorch: Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond 2nd ed. Edition

This book has been a valuable resource in my deep learning journey. It provides a solid overview of the PyTorch framework, covering everything from the basics to advanced topics.The author explains complex concepts in a clear and straightforward way, making it easy to follow. With numerous examples and code snippets, it's simple to apply the lessons to real-world projects, helping you save time and effort when building deep learning models.Overall, I recommend Mastering PyTorch to anyone wanting to deepen their knowledge of deep learning with Python, this book offers valuable insights.

Pretty good book, covers the basics and the advanced topics. Good source code examples.

I keep this book on my desk with me while I work. It's great to get immediate quick but also in depth explanations. I particularly like the code snippets that are present to demonstrate various models. Definitely, if one is using pyTorch on a regular basis, this is a good resource to have closeby. Also good for beginners who are getting into the subject.

Great for advanced learners and those that love math and science.

Here's my experience with the book:Positives:- Theoretical parts are easy to understand and the coding exercises make it challenging and engaging- Covers a breadth of topics, not being covered by other similar books in the field- Gives excellent deep dive into deep learning engineeringSuggestions:- Would be good to have torchrec based topics for recommendation systems in the next releaseOverall this book is a must read. The coding solutions to several complicated ML problems along with the ease of access to the code in a GitHub repo is a good resource as part of any ML developer’s machine learning toolkit.

As I read/studied the examples I was impressed. I was feeling confident that I could make the switch from tensorFlow to PyTorch. Then I started to look at "Using PyTorch to fine-tune AlexNet" I was unable to load 'hymenopters_data' from the downloaded data set. I kept getting "No such file or directory 'hymenopters/train'". I'm using Ubuntu

This book is quite hands-on. There is code in every chapter. I've done a few chapters and the code from github runs for me so far.Initial chapters are a good easy read to understand deep learning concepts. Last chapters in the book are more on the practical side. Chapter on HuggingFace could be longer and split into 2 chapters as the author covers lots of content in 1 chapter. I expected more on LLMs, but the book is overall good to get comfortable working with PyTorch. Definitely recommend reading it once.P.S. pretty big book

I started self-education in an AI field using a practical approach. So, this book is everyday program copilot (as my cat :) ).