Free Ebook Deep Learning Cookbook: Practical Recipes to Get Started Quickly
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Deep Learning Cookbook: Practical Recipes to Get Started Quickly
Free Ebook Deep Learning Cookbook: Practical Recipes to Get Started Quickly
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About the Author
Douwe Osinga is an experienced Software Engineer, formerly with Google, and founder of three startups. He maintains a popular software project website, partly focused on machine learning(https://douweosinga.com/projects/machine_learning).
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Product details
Paperback: 252 pages
Publisher: O'Reilly Media; 1 edition (June 23, 2018)
Language: English
ISBN-10: 149199584X
ISBN-13: 978-1491995846
Product Dimensions:
7 x 0.5 x 9.1 inches
Shipping Weight: 15.2 ounces (View shipping rates and policies)
Average Customer Review:
5.0 out of 5 stars
5 customer reviews
Amazon Best Sellers Rank:
#201,736 in Books (See Top 100 in Books)
This book makes understanding deep learning a breeze. The simple diagrams and tone of the writing make it approachable and fun. The Python examples in the book are shared on GitHub to allow anyone to jump in with some Python knowledge.Highly highly recommend if you want to bring your deep learning from 0 to 60 fast!
This is a great book for anybody who has a decent background in writing software and an interest in getting started with Deep Learning. The book covers a wide variety of topics ranging from text classification and generation to image and music processing. The Python Notebooks accompanying the book make it easy to follow the code. The book is black and white which isn't ideal for some outputs. In these cases the Notebooks come in handy too.
Excellent resource and fast shipping
There are two classes of books for deep learning: the theoretical ones and the practical ones. If you are starting, then my suggestion is to pick a book for each class and iterate back and forward between classes.Now, this book is in my top 3 books for the 'practical ones' class. Very well, written with large coverage of the most modern aspects of Deep learning. I particularly enjoyed the chapter on music analysis - which is normally not covered by other books - and all the comprehensive discussions about embeddings. Only Deep Reinforcement Learning is not covered by the book and that's the reason why I do not position the book in the top 2. Overall it's a book I definitively recommend. The github repository is also well done.
Very useful book, beautifully written and well explained.
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