Deep Learning with Keras: Implementing deep learning models and neural networks with the power of Python

I felt compelled to write a review because I really think this is an exceptionally good book under the circumstances. When I say "under the circumstances", I mean given the fact that deep learning is a challenging topic to explain and requires both a theoretic and practical approach to be appreciated. The author clearly avoids getting bogged down with the theoretical aspects and I can appreciate why since a thorough theoretical understanding would require a separate book in it's own right.This book will not help you understand the theory or underlying mathematics. However, if you already understand the theory and want to learn to use a package like Keras then this is the book for you.This book stands out because it gives details about the implementation aspects of coding many different deep learning models that you will hear about in the literature and in the field. For example, LeNet, ResNet, etc. among many others are demonstrated through out the book.Generally speaking, topics in deep learning are not easy to explain to the average reader and I think the author recognizes this difficulty and chooses to place his focus on demonstrating how to implement deep learning methods and being careful to explain what the different modules do and their respective parameters.In my view, this book is very suitable for Data Scientists who already know the spectrum of machine learning models and techniques and want to get their hands dirty as fast as possible with deep learning. This book is a much better practical book for deep learning than the popular book by Aurélien Géron called "Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems". I have looked at many deep learning books and in my view this one did the best job is getting me comfortable with implementing deep learning models on my own.The one thing that I found the book was lacking is that it's final chapter on AI and reinforcement learning did not seem as thorough and detailed as the other chapters in the book. Having reviewed many books in the area of deep learning, I can honestly say this is probably the best book I have come across so far. However, I came to this book already having a solid understand of deep learning theory.

Read half of it. The examples are ok but the theory explanation of what you are doing are very basic - not enough to understand the theory without some further background. And the coding part is just a cookbook presentation - here it is. Not a lot of depth.Have not tried to run the code samples yet.I was hoping for a bit more. Maybe given that keras sits on top of tensorflow or Theano there is just not as much to say?This book contains many code examples and log traces showing execution of the code. The code and log traces are formatted like code on a computer screen and look very washed out (print book). Bolder fonts and a little ink would have made this book a lot easier to read. This also gives you an idea what this book is like - screen shots of code followed by screen shots of code execution.

If you peruse the Table of Contents you'll see that this book covers recent research (with references provided) over a number of cutting-edge topics. For each topic, the authors spend some time introducing the concepts and provide working experiments. This, in my opinion, is the best feature of the book and makes it well worth the price. The book also includes an introductory chapter developing a set of classifiers for the MNIST set, a chapter on Keras installation over a variety of platforms, and a chapter with an overview of the Keras API. I would have liked to see more about the inner-workings of Keras-- how it works, not just how to use it. Overall, I highly recommend this book to technically-proficient deep learning enthusiasts. For those just starting out with deep learning, python programming, or both, you will want to supplement this book with more introductory material.

I needed a reference book to use Keras that is a user-oriented library for easy modeling of neural networks in Python. Unlike some low reviews on the book, it turned out to be exactly what I expected and what its title said, Implementing deep learning models and neural networks with Keras in Python.If you want to know more about theory of deep learning, you should refer to other deep learning books. If you want to know how Keras API internally works, you may want to look at other books on Tensorflow or Theano that was low level API for Keras and with which you can define neural networks in node-level. But if you want to flexibly and easily build a NN model with fewer lines of code, this book might be good for you.

This is a useful book. In essence it is a series of examples with the code associated. The code works and the comments are helpful. It doesn't elaborate any of the techniques, so you will have to look elsewhere for deeper understanding. If you know where to look for examples then it won't be much use to you. If you don't know where to look then it is a very useful guidebook.

I wasn’t a huge fan of this text. I found online resources to be more helpful in general and the book was laid out in an odd order. Also, it was pretty pricey compared to most books/resources for this type of thing.

Served its purpose well, however I feel it should definitely be supplemented on a book with more theory on neural networks, and the chapter on reinforcement learning started off well but had way to much code without explanation.

Major part of book is walking through source code from keras.ai and github repositories, with just hand waving explanation. Half the book is copy & pasted source code and images from code runs. There is no real depth of explanation. If you want a quick introduction to Keras this could be a good book for you. If you are looking to get a deeper understanding then this book is a miss.

It starts out pretty well, and quickly gets you going with the core Deep learning techniques with Keras, a beautiful API for deep learning. Pretty reasonable overview of each technique, which doesn't get in the way of getting on with the code examples. pretty good coverage through the book.The only problem is that it being one of the first books out on Keras, is that some of the code examples from the middle of the book onwards starts to become reliant upon third party code and GIT repositories which don't fully work and are not fully supported, which spoils the otherwise good progress.Another warning is that you will need a POC with a strong Nvidia Graphics card with TensorFlow installed (Now very easy with Anaconda), otherwise you will be in for long waits whilst your Deep learning experiments run (Applies to all DL environments.)

Seems like the writer was in a hurry... I have to be constantly on the net to get real explanations ... lack of good examples (e.g in recursive NN is mandatory a timeseries example .. simple like predick y=sen(x) fucntion ... or in convnets other examples besides images, convnets are badly explained ). Internet is free and the book does not add much to free info.

The worst technical book I've ever bought. Most code examples are wrong, indentation completely random, new lines missing. Lines randomly commented out. If you don't have the time/knowhow to fix all errors, just buy something else.

Excellent introduction to Keras and machine learning. Ideal for newbies to this field. Great examples covering the breadth of current state of the discipline.

Very basic book.Chapter 1,5,6,7 are explained in good way. But that u can find in other books like machine learning in python by jason brownie