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Great resource, encyclopedic manual
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.
Excellent guide book
Great for advanced learners and those that love math and science.
Must read for any ML engineer
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 developerâs machine learning toolkit.
cool PyTorch guide with hands-on code. recommend
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
Converting from TensorFlow to PyTorch
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 is a book for me
I started self-education in an AI field using a practical approach. So, this book is everyday program copilot (as my cat 🙂 ).
A very useful and practical guide to learn about LLMs
I enjoyed reading “Mastering PyTorch” because it offered a hands-on, practical approach to deep learning with PyTorch. The clear explanations, real-world examples, and up-to-date content kept me engaged and informed. The expert insights and comprehensive coverage made complex concepts accessible and relevant, significantly enhancing my learning experience.
Practical survey of applying PyTorch for deep ML architectures
This book provides a balance of high-level concepts and multitude of coded examples for experienced ML programmers that are looking for a practical onboarding to PyTorch. It includes examples of TensorFlow->PyTorch, many modern models/architectures, as well as engineering topics including (distributed) training/deployment and integration with HuggingFace & mobile/web.Readers should be adept in modern Python programming (mostly Jupyter notebooks), and likely will need to adapt coded examples given the fast-pace of change in this ecosystem (e.g., expect to make minor modifications to account for incompatibilities between library dependencies, as well as varying hardware setups).
Mit diesem Buch bekommt man einen sehr guten und umfassenden Einblick in Pytorch. Das Wissen kann mit zahlreichen Beispielen geübt und vertieft werden.Diese schwierige Bibliothek wird dennoch sehr gut und einfach zu verstehen erklärt.Klare Empfehlung
Absolutely love this book both as a reference and to learn new techniques.I’m a ML researcher converting from Tensorflow to Pytorch and wanted a reference guide as I made the transition. The hands-on code examples were super useful to get up and running, and much more clearly explained than just trying to Google what to do.The pieces on engineering included a bunch of optimisations I hadn’t considered in in the past, so I ended up learning a lot more than I anticipated. This book is very well-rounded and considers both the practical application and the theory behind it.I would highly recommend to any ML researcher or engineer!
I’m currently working my way through “Mastering PyTorch – Second Edition” by Ashish Ranjan Jha and finding it to be an indispensable resource for both beginners and seasoned practitioners in deep learning with PyTorch. The book is thorough, diving into both fundamental and advanced topics like CNNs, Transformers, and GNNs. Each chapter doesn’t just discuss theory but also walks you through practical implementations which is great for hands-on learning.The sections on Transformers and Graph Neural Networks are particularly insightful, providing practical applications and up-to-date information on these cutting-edge technologies. Although I am still in the process of reading and have much to learn, this book has already proven to be a comprehensive guide that enhances understanding through active engagement and experimentation with the exercises provided.Highly recommended for anyone interested in mastering PyTorch and delving deep into the realm of artificial intelligence.
The book is a must read for anyone looking to learn beyond basics and delve deeper into hands-on problem solving . What separates this book from the others is the breadth of topics aiming to solve real business problems than just explaining the concepts.
“Mastering PyTorch, Second Edition” is an excellent resource for anyone interested in deep learning. This updated edition includes the latest advances, such as transformers, and so I think it’s worth getting even (and maybe especially) if you already own the first edition.As in the first edition, the book is packed with code snippets that get you started in no time.It also covers all of the practical aspects of downloading a dataset, configuring your environment, and so on.I really appreciate the Chapter on integrating PyTorch and HuggingFace, as HuggingFace is quickly becoming one of the most important libraries in machine learning.