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

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Keras Installation and API

In the previous chapter, we discussed the basic principles of neural networks and provided a few examples of nets that are able to recognize MNIST handwritten numbers.

This chapter explains how to install Keras, Theano, and TensorFlow. Step by step, we will look at how to get the environment working and move from intuition to working nets in very little time. Then we will discuss how to install on a dockerized infrastructure based on containers, and in the cloud with Google GCP, Amazon AWS, and Microsoft Azure. In addition to that, we will present an overview of Keras APIs, and some commonly useful operations such as loading and saving neural networks' architectures and weights, early stopping, history saving, checkpointing, and interactions with TensorBoard and Quiver. Let us start.

By the end of this chapter, we will have covered the following topics:

Installing and configuring...

Key benefits

Implement various deep learning algorithms in Keras and see how deep learning can be used in games

See how various deep learning models and practical use-cases can be implemented using Keras

A practical, hands-on guide with real-world examples to give you a strong foundation in Keras

Description

This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing with recognition of handwritten digit images, classification of images into different categories, and advanced objects recognition with related image annotations. An example of identification of salient points for face detection is also provided. Next you will be introduced to Recurrent Networks, which are optimized for processing sequence data such as text, audio or time series. Following that, you will learn about unsupervised learning algorithms such as Autoencoders and the very popular Generative Adversarial Networks (GANs). You will also explore non-traditional uses of neural networks as Style Transfer. Finally, you will look at reinforcement learning and its application to AI game playing, another popular direction of research and application of neural networks.

What you will learn

Optimize step-by-step functions on a large neural network using the Backpropagation algorithm

Fine-tune a neural network to improve the quality of results

Use deep learning for image and audio processing

Use Recursive Neural Tensor Networks (RNTNs) to outperform standard word embedding in special cases

Identify problems for which Recurrent Neural Network (RNN) solutions are suitable

Explore the process required to implement Autoencoders

Evolve a deep neural network using reinforcement learning

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Kyle Mar 07, 2023

Fast and convenient, no surprises.

Amazon Verified review

Amazon Customer Sep 07, 2017

This is definitely one of the best resources if you want to learn Keras.

Arbaaz Qureshi Jun 23, 2018

Good book, for readers who are familiar with deep learning concepts and Keras and wish to improve them further. Correct the errors of the code section, in the next version.

Tae S. Shin Dec 09, 2018

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.

Melvin Dec 30, 2017

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.

Parameters	Values
`image_dim_ordering`	Can be either `tf` for the TensorFlow image ordering or `th` for Theano image ordering
`epsilon`	The `epsilon` value used during computation
`floatx`	Can be either `float32` or `float64`
`backend`	Can be either `tensorflow` or `theano`

Parameters	Values
`image_dim_ordering`	Can be either `tf` for the TensorFlow image ordering or `th` for Theano image ordering
`epsilon`	The `epsilon` value used during computation
`floatx`	Can be either `float32` or `float64`
`backend`	Can be either `tensorflow` or `theano`

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

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Deep Learning with Keras

Keras Installation and API

Installing Keras

Step 1 — install some useful dependencies

Configuring Keras

Installing Keras on Docker

Installing Keras on Google Cloud ML

Installing Keras on Amazon AWS

Installing Keras on Microsoft Azure

Installing Keras

Step 1 — install some useful dependencies

Configuring Keras

Note

Installing Keras on Docker

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