Book description
Get to grips with the basics of Keras to implement fast and efficient deeplearning models
About This Book
 Implement various deeplearning algorithms in Keras and see how deeplearning can be used in games
 See how various deeplearning models and practical usecases can be implemented using Keras
 A practical, handson guide with realworld examples to give you a strong foundation in Keras
Who This Book Is For
If you are a data scientist with experience in machine learning or an AI programmer with some exposure to neural networks, you will find this book a useful entry point to deeplearning with Keras. A knowledge of Python is required for this book.
What You Will Learn
 Optimize stepbystep functions on a large neural network using the Backpropagation Algorithm
 Finetune 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
In Detail
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 hand written 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 (GAN). You will also explore nontraditional 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.
Style and approach
This book is an easytofollow guide full of examples and realworld applications to help you gain an indepth understanding of Keras. This book will showcase more than twenty working Deep Neural Networks coded in Python using Keras.
Downloading the example code for this book. You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the code file.
Publisher resources
Table of contents
 Preface

Neural Networks Foundations
 Perceptron
 Multilayer perceptron — the first example of a network

A real example — recognizing handwritten digits
 Onehot encoding — OHE
 Defining a simple neural net in Keras
 Running a simple Keras net and establishing a baseline
 Improving the simple net in Keras with hidden layers
 Further improving the simple net in Keras with dropout
 Testing different optimizers in Keras
 Increasing the number of epochs
 Controlling the optimizer learning rate
 Increasing the number of internal hidden neurons
 Increasing the size of batch computation
 Summarizing the experiments run for recognizing handwritten charts
 Adopting regularization for avoiding overfitting
 Hyperparameters tuning
 Predicting output
 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
 Getting started with Keras architecture
 An overview of predefined neural network layers
 An overview of predefined activation functions
 An overview of losses functions
 An overview of metrics
 An overview of optimizers
 Some useful operations
 Saving and loading the weights and the architecture of a model
 Callbacks for customizing the training process
 Summary
 Deep Learning with ConvNets
 Generative Adversarial Networks and WaveNet
 Word Embeddings
 Recurrent Neural Network — RNN
 Additional Deep Learning Models
 AI Game Playing
 Conclusion
Product information
 Title: Deep Learning with Keras
 Author(s):
 Release date: April 2017
 Publisher(s): Packt Publishing
 ISBN: 9781787128422
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