deep learning with python book

You'll need another book for theory such as deep learning(Ian, Yoshua, Aaron) if you want to study further (whether good or not, Keras abstracts away internal functions of the neural networks). The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. Not only a top researcher in the field, and the mastermind behind Keras (which is used throughout the book) but Chollet can also explain the ideas too, which makes this one of the few useful additions to the growing number of books on deep learning. After reading a few chapters, readers(with some programming experience) will be able to apply the techniques easily to real world datasets or to participate in Kaggle (API is much simpler than that of TensorFlow). Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding … And it might not be enough reading it only once.

Can't wait for the second edition of the book There are no discussion topics on this book yet.

The online version of the book is now complete and will remain available online for free.

I found it a challenging read in places, mostly due to me trying to understand the theory/mathematics (and how they're implemented in code) from the book. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications.Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library.

Furthermore, code samples in the book are very easy to follow and implement. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. Intermediate

To see what your friends thought of this book, The author skips the math and most of the technical explanations and instead focuses more on the engineering and best practices. Although there are some tutorials online about the usage of Keras, it is convenient to have a book which covers most of API with examples.

It describes the in's and out's of deep learning with a thorough verbal descriptions, mathematical expressions, graphical flow-diagrams, and Python code. A very practical and up to the point book on deep learning techniques in python by the guru who created the Keras library. Readers need intermediate Python skills. Hence all the examples in the book are in Keras.

Highly recommend anyone who want to get into the field to start with this first, write their own code and tinker, and then go through the more theoretical books such as Deep Learning by Goodfellow et al which is very theoretical, broad and academic.Deep learning is the newest fad in the computer science world.

Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library.

Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library.

All books for intermediate Python programmers The author provided easy-to-follow github repositories to actually work on the projects he walked through in the book. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. The clearest explanation of deep learning I have come across...it was a joy to read. It's practical, but does not sacrifice enough theory for you to comprehend the material.Welcome back. You should understand the basics of machine learning. Refresh and try again. Comment on this book's Logics of right generalization. I plan to use this book as a reference whenever I do my deep learning projects Nevertheless, a very useful resource.This is the guide to deep learning using Keras, the most popular tool, from the creator himself.Deep learning is the newest fad in the computer science world. Some things took awhile to fully process, but that's expected with the depth the author gets into. As an ML/AI PhD, I found his descriptions of the various areas of deep learning to be thorough yet gentle. I've set up a network for a competition on Kaggle, and I also created a bot that has learned how to play the videogame Rocket League (using recordings of screen captures and button presses).