Deep Learning with Keras

By : Antonio Gulli , Sujit Pal

3.5 (20)

Buy this Book

Deep Learning with Keras

3.5 (20)

By: Antonio Gulli , Sujit Pal

Buy this Book

Overview of this book

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.

Preface

Mission

How deep learning is different from machine learning and artificial intelligence

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

Neural Networks Foundations

Perceptron

Multilayer perceptron — the first example of a network

A real example — recognizing handwritten digits

A practical overview of backpropagation

Towards a deep learning approach

Summary

Keras Installation and API

Installing Keras

Configuring Keras

Installing Keras on Docker

Installing Keras on Google Cloud ML

Installing Keras on Amazon AWS

Installing Keras on Microsoft Azure

Keras API

Callbacks for customizing the training process

Summary

Deep Learning with ConvNets

Deep convolutional neural network — DCNN

An example of DCNN — LeNet

Recognizing CIFAR-10 images with deep learning

Very deep convolutional networks for large-scale image recognition

Summary

Generative Adversarial Networks and WaveNet

What is a GAN?

Deep convolutional generative adversarial networks

Keras adversarial GANs for forging MNIST

Keras adversarial GANs for forging CIFAR

WaveNet — a generative model for learning how to produce audio

Summary

Word Embeddings

Distributed representations

word2vec

Exploring GloVe

Using pre-trained embeddings

Summary

Recurrent Neural Network — RNN

SimpleRNN cells

RNN topologies

Vanishing and exploding gradients

Long short term memory — LSTM

Gated recurrent unit — GRU

Bidirectional RNNs

Stateful RNNs

Other RNN variants

Summary

Additional Deep Learning Models

Keras functional API

Regression networks

Unsupervised learning — autoencoders

Composing deep networks

Customizing Keras

Generative models

Summary

AI Game Playing

Reinforcement learning

Example - Keras deep Q-network for catch

The road ahead

Summary

Conclusion

Keras 2.0 — what is new

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SimpleRNN cells

Traditional multilayer perceptron neural networks make the assumption that all inputs are independent of each other. This assumption breaks down in the case of sequence data. You have already seen the example in the previous section where the first two words in the sentence affect the third. The same idea is true of speech—if we are having a conversation in a noisy room, I can make reasonable guesses about a word I may not have understood based on the words I have heard so far. Time series data, such as stock prices or weather, also exhibit a dependence on past data, called the secular trend.

RNN cells incorporate this dependence by having a hidden state, or memory, that holds the essence of what has been seen so far. The value of the hidden state at any point in time is a function of the value of the hidden state at the previous time step and the value of the input at the current...