Keras Deep Learning Cookbook

Keras Deep Learning Cookbook

By : Rajdeep Dua, Sujit Pal, Manpreet Singh Ghotra

Buy this Book

Keras Deep Learning Cookbook

By: Rajdeep Dua, Sujit Pal, Manpreet Singh Ghotra

Buy this Book

Overview of this book

Keras has quickly emerged as a popular deep learning library. Written in Python, it allows you to train convolutional as well as recurrent neural networks with speed and accuracy. The Keras Deep Learning Cookbook shows you how to tackle different problems encountered while training efficient deep learning models, with the help of the popular Keras library. Starting with installing and setting up Keras, the book demonstrates how you can perform deep learning with Keras in the TensorFlow. From loading data to fitting and evaluating your model for optimal performance, you will work through a step-by-step process to tackle every possible problem faced while training deep models. You will implement convolutional and recurrent neural networks, adversarial networks, and more with the help of this handy guide. In addition to this, you will learn how to train these models for real-world image and language processing tasks. By the end of this book, you will have a practical, hands-on understanding of how you can leverage the power of Python and Keras to perform effective deep learning

Title Page

Packt Upsell

Contributors

Preface

Free Chapter

Keras Installation

Introduction

Installing Keras on Ubuntu 16.04

Installing Keras with Jupyter Notebook in a Docker image

Installing Keras on Ubuntu 16.04 with GPU enabled

Working with Keras Datasets and Models

Load data from a CSV file

Models in Keras – getting started

Sequential models

Shared layer models

Keras functional APIs

Keras functional APIs – linking the layers

Image classification using Keras functional APIs

Data Preprocessing, Optimization, and Visualization

Feature standardization of image data

Sequence padding

Model visualization

Optimization

Common code for samples

Optimization with stochastic gradient descent

Optimization with Adam

Optimization with AdaDelta

Optimization with RMSProp

Classification Using Different Keras Layers

Introduction

Classification for breast cancer

Classification for spam detection

Implementing Convolutional Neural Networks

Introduction

Cervical cancer classification

Digit recognition

Generative Adversarial Networks

Introduction

Basic GAN

Boundary seeking GAN

DCGAN

Recurrent Neural Networks

Introduction

Simple RNNs for time series data

LSTM networks for time series data

Time series forecasting with LSTM

Sequence to sequence learning for the same length output with LSTM

Natural Language Processing Using Keras Models

Introduction

Word embedding

Sentiment analysis

Text Summarization Using Keras Models

Introduction

Text summarization for reviews

Reinforcement Learning

Introduction

The CartPole game with Keras

Dueling DQN to play Cartpole

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LSTM networks for time series data

In this recipe, we will learn what LSTM networks are and how can they be leveraged to better predict time series data with long-term memory characteristics.

LSTM networks

LSTM is designed to avoid the long-term dependency problem. It remembers the information for a longer period of time.

All recurrent neural networks have the form of a chain of repeating modules of a neural network. In standard RNNs, this repeating module will have a very simple structure, such as a single tanh layer. LSTMs also have this chain-like structure, but the repeating module has a different structure.

There are four layers, interacting in a very special way, as shown in the following diagram:

Reference: http://colah.github.io/posts/2015-08-Understanding-LSTMs/

We will not go into more detail on how an LSTM works, but focus on how it is used in Keras.

LSTM memory example

In this recipe, we will learn, with a simple example, how an LSTM network remembers the value in a step from the distant...

Keras Deep Learning Cookbook

By : Rajdeep Dua, Sujit Pal, Manpreet Singh Ghotra

Keras Deep Learning Cookbook

By: Rajdeep Dua, Sujit Pal, Manpreet Singh Ghotra

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