Keras Deep Learning Cookbook · Over 80 Recipes for Implementing Deep Neural Networks in Python by Dua, Rajdeep -- Read -- Imperial Library of Trantor

Index

Title Page Copyright

Neural Network Programming with TensorFlow

Credits About the Authors About the Reviewer www.PacktPub.com

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Customer Feedback Preface

What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support

Downloading the example code Downloading the color images of this book Errata Piracy Questions

Maths for Neural Networks

Understanding linear algebra

Environment setup

Setting up the Python environment in Pycharm

Linear algebra structures

Scalars, vectors, and matrices Tensors

Operations

Vectors Matrices Matrix multiplication Trace operator Matrix transpose Matrix diagonals Identity matrix Inverse matrix

Solving linear equations Singular value decomposition Eigenvalue decomposition Principal Component Analysis

Calculus

Gradient Hessian Determinant

Optimization

Optimizers

Summary

Deep Feedforward Networks

Defining feedforward networks Understanding backpropagation Implementing feedforward networks with TensorFlow Analyzing the Iris dataset

Code execution

Implementing feedforward networks with images

Analyzing the effect of activation functions on the feedforward networks accuracy

Summary

Optimization for Neural Networks

What is optimization? Types of optimizers Gradient descent

Different variants of gradient descent Algorithms to optimize gradient descent

Which optimizer to choose

Optimization with an example

Summary

Convolutional Neural Networks

An overview and the intuition of CNN

Single Conv Layer Computation CNN in TensorFlow

Image loading in TensorFlow

Convolution operations

Convolution on an image Strides

Pooling

Max pool Example code

Average pool

Image classification with convolutional networks

Defining a tensor for input images and the first convolution layer

Input tensor First convolution layer Second convolution layer Third convolution layer Flatten the layer Fully connected layers Defining cost and optimizer Optimizer First epoch Plotting filters and their effects on an image

Summary

Recurrent Neural Networks

Introduction to RNNs

RNN implementation

Computational graph

RNN implementation with TensorFlow

Computational graph

Introduction to long short term memory networks

Life cycle of LSTM LSTM implementation

Computational graph

Sentiment analysis

Word embeddings Sentiment analysis with an RNN

Computational graph

Summary

Generative Models

Generative models

Discriminative versus generative models Types of generative models

Autoencoders GAN Sequence models

GANs

GAN with an example Types of GANs

Vanilla GAN Conditional GAN Info GAN Wasserstein GAN Coupled GAN

Summary

Deep Belief Networking

Understanding deep belief networks

DBN implementation

Class initialization RBM class

Pretraining the DBN

Model training Predicting the label Finding the accuracy of the model DBN implementation for the MNIST dataset

Loading the dataset Input parameters for a DBN with 256-Neuron RBM layers Output for a DBN with 256-neuron RBN layers

Effect of the number of neurons in an RBM layer in a DBN

An RBM layer with 512 neurons An RBM layer with 128 neurons Comparing the accuracy metrics

DBNs with two RBM layers Classifying the NotMNIST dataset with a DBN Summary

Autoencoders

Autoencoder algorithms Under-complete autoencoders Dataset Basic autoencoders

Autoencoder initialization AutoEncoder class Basic autoencoders with MNIST data

Basic autoencoder plot of weights Basic autoencoder recreated images plot

Basic autoencoder full code listing Basic autoencoder summary

Additive Gaussian Noise autoencoder

Autoencoder class Additive Gaussian Autoencoder with the MNIST dataset

Training the model Plotting the weights

Plotting the reconstructed images Additive Gaussian autoencoder full code listing Comparing basic encoder costs with the Additive Gaussian Noise autoencoder Additive Gaussian Noise autoencoder summary

Sparse autoencoder

KL divergence

KL divergence in TensorFlow

Cost of a sparse autoencoder based on KL Divergence

Complete code listing of the sparse autoencoder Sparse autoencoder on MNIST data Comparing the Sparse encoder with the Additive Gaussian Noise encoder

Summary

Research in Neural Networks

Avoiding overfitting in neural networks

Problem statement Solution Results

Large-scale video processing with neural networks

Resolution improvements Feature histogram baselines Quantitative results

Named entity recognition using a twisted neural network

Example of a named entity recognition Defining Twinet Results

Bidirectional RNNs

BRNN on TIMIT dataset

Summary

Getting started with TensorFlow

Environment setup TensorFlow comparison with Numpy Computational graph

Graph Session objects Variables Scope Data input Placeholders and feed dictionaries

Auto differentiation TensorBoard

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