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Top (max 10) reviews: Deep Learning with Python.

5.0 out of 5.0    7 total reviews.

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by Melvin on Jan. 28, 2018

This is an exceptional book. The author clearly has put a lot of thought into how to present topics and what is the best strategy for teaching the concepts. It is very hard to find a book that is written as clearly and thoughtfully as this one. The author explains all the basics and clears up all the ambiguities that you may find in other books. This is the type of book that can be read by a complete beginner and bring them up to speed very quickly.
I cannot say enough about how good this book really is. If you can only buy one book on deep learning, this should be the one you buy.

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by Thomas D France Jr on Dec. 30, 2017

The author provides clear and cogent explanations of some of the hottest topics in the field (e.g. convolutional neural networks, recurrent neural networks, generative adversarial networks) with worked examples that are easy to reproduce on a laptop. I particularly appreciate the author's advice on best-practices for setting up various types of networks -- which approaches work best for certain types of problems, selecting network topologies, optimizers, activation functions, etc. The book is light on theory, by design, so if you want a fuller treatment of the mathematical underpinnings see Goodfellow, et al. 
 . The book has excellent Jupyter notebooks to accompany and illustrate all relevant topics.

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by Christian N. Hagel-Sorensen on Jan. 1, 2018

I have taken the machine learning class in Coursera but the first two chapters in this book brough a whole new level of clarity to all the concepts. I finally really get what each of the parts of the training and optimization do. Also love the explanations in code instead of in mathematics. I feel I have a much better intuition about vectors than I did before.

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by Robert McKee on Jan. 9, 2018

Excellent how-to on deep learning with Keras, with a very good introductory primer on machine learning. Connects many dots in very clear, understandable way. Excellent examples with many practical implementation tips and clearly annotated source code.

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by Claudio Rdgz on Jan. 26, 2018

I started learning Deep Learning from Udacity. For those that don't know, DL is a subset of Machine Learning. The main issue with Deep Learning is that it requires a lot of experimentation. Just a basic example takes hours to train and to see it work. There are many applications of Deep Learning, and you can bet that adapting the tools to your needs is going to be a very thoughtful experience.
Deep Learning with Python will walk you through it all. I was able to learn way of implementing DL to areas I was trying to break through before with a lot of ease. It also walks you through how to use DL on a Cloud Environment, which is going to be an invaluable tool for your career if it's anywhere related to software.
Keras (the framework you'll use in this book) is a great tool for you to start experimenting and understanding the concepts of Neural Networks. And you will be able to build your own networks after a few code examples from the book.
Things this book will give you:
- A great framework for you to experiment, craft, learn, and understand more about Neural Networks.
- A great set of examples so that you can see how you can adapt DL to your specific domain problem.
- Cloud knowledge on how to leverage it to use Deep Learning
I'm going through my second reading of the book, taking advantage of Colaboratory, a Google research project created to help disseminate machine learning education and research.

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by Sumit Pal on Jan. 27, 2018

One of the best books on Deep Learning with Keras in the market. The books is written by the developer of Keras and the best aspect of the book is the simplicity of approach and clarity of explanation both textual and the pictorial representations.
The book is the ideal for starters who have no background in Deep Learning but have worked with ML algorithms. It explains all the basic fundamental building blocks necessary to understand, develop and run in production a NN based approach to solving problems.
The book clearly explains how these systems work and what makes them tick.
The book is filled with working code and extremely good explanation of the code and to what is being done and why and in most places why certain parameters are chosen.
I highly recommend this book for starters and intermediate level Deep Learning professionals

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by mochabeans on Dec. 26, 2017

Excellent book on deep learning. Enough basic grounds covered before final take off.