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Product details
File Size: 12392 KB
Print Length: 256 pages
Simultaneous Device Usage: Unlimited
Publisher: O'Reilly Media; 1 edition (January 22, 2019)
Publication Date: January 22, 2019
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B07N17TMFH
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Amazon Best Sellers Rank:
#141,141 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
This book sets out to serve as a text for those new to deep learning and it does just that. For those removed from research, the mathematics of other introductory texts can be a bit distracting. This text takes a refreshing step back to show what you might see in the research as code. It runs the gamut from training your own models from scratch to transfer learning in the course of a few hundred pages -- what a feat!As an editorial note, I find the bitly notes a bit noisy -- I might have used the referent links in their place, but no big gripe.Bravo, Delip and Brian!
Half of the book goes over basics. The book would be worth it at $30. Pretty disappointed, I thought there would be more in depth coverage of NLP topics.This book might be more useful for newcomers.
An awesome book to dive into your NLP and deep learning journey with PyTorch. Delip and Brian have done a great job in explaining NLP concepts clearly and demonstrating them in code in each chapter to solve practical NLP tasks.Disclaimer: I was one of the technical reviewer of the book =)
I generally do not learn about NLP using books. I generally read papers, blogposts, or directly dive into github repositories. As someone pointed out in a previous comment, this book is not a bible 1000 pages book for NLP.. If you're looking for a comprehensive book covering all machine learning and NLP this is not the place.Why did I love this book? When you learn using blogposts or github pages, there are a lot of unsaid things, a lot of stuffs that are actually hidden under the hood, whether the author does not take time to explain, or whether the author does not know himself about the details. This book establishes a very solid foundation for deep NLP. It explains all the basics usage of Pytorch, and how to build growing building blocks. It covers all the necessary to use any kind of embeddings, run multi-task settings, experiment with custom sequence-to-sequence models etc...After reading this book, I felt confident that I could build my own Allennlp / Fast.ai / PyText / Flair library, which is a very exciting perspective after reading a book!Thank you!
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