TensorFlow 1.x deep learning cookbook : over 90 unique recipes to solve artificial-intelligence driven problems with Python / Antonio Gulli, Amita Kapoor.

Format:

Book

Author/Creator:

Gulli, Antonio, author.

Kapoor, Amita, author.

Language:

English

Subjects (All):

Python (Computer program language).

Physical Description:

1 online resource (1 volume) : illustrations

Edition:

1st edition

Place of Publication:

Birmingham, England ; Mumbai, [India] : Packt, 2017.

System Details:

text file

Summary:

Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.x About This Book Skill up and implement tricky neural networks using Google's TensorFlow 1.x An easy-to-follow guide that lets you explore reinforcement learning, GANs, autoencoders, multilayer perceptrons and more. Hands-on recipes to work with Tensorflow on desktop, mobile, and cloud environment Who This Book Is For This book is intended for data analysts, data scientists, machine learning practitioners and deep learning enthusiasts who want to perform deep learning tasks on a regular basis and are looking for a handy guide they can refer to. People who are slightly familiar with neural networks, and now want to gain expertise in working with different types of neural networks and datasets, will find this book quite useful. What You Will Learn Install TensorFlow and use it for CPU and GPU operations Implement DNNs and apply them to solve different AI-driven problems. Leverage different data sets such as MNIST, CIFAR-10, and Youtube8m with TensorFlow and learn how to access and use them in your code. Use TensorBoard to understand neural network architectures, optimize the learning process, and peek inside the neural network black box. Use different regression techniques for prediction and classification problems Build single and multilayer perceptrons in TensorFlow Implement CNN and RNN in TensorFlow, and use it to solve real-world use cases. Learn how restricted Boltzmann Machines can be used to recommend movies. Understand the implementation of Autoencoders and deep belief networks, and use them for emotion detection. Master the different reinforcement learning methods to implement game playing agents. GANs and their implementation using TensorFlow. In Detail Deep neural networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. The entire world is filled with excitement about how deep networks are revolutionizing artificial intelligence. This exciting recipe-based guide will take you from the realm of DNN theory to implementing them practically to solve the real-life problems in artificial intelligence domain. In this book, you will learn how to efficiently use TensorFlow, Google's open source framework for deep learning. You will implement different deep learning networks such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learnin...

Contents:

Cover

Copyright

Credits

About the Authors

About the Reviewers

www.PacktPub.com

Customer Feedback

Dedication

Table of Contents

Preface

Chapter 1: TensorFlow - An Introduction

Introduction

Installing TensorFlow

Getting ready

How to do it...

How it works...

There's more...

Hello world in TensorFlow

Understanding the TensorFlow program structure

Working with constants, variables, and placeholders

Performing matrix manipulations using TensorFlow

Using a data flow graph

Migrating from 0.x to 1.x

Using XLA to enhance computational performance

Invoking CPU/GPU devices

TensorFlow for Deep Learning

There's more

Different Python packages required for DNN-based problems

See also

Chapter 2: Regression

Choosing loss functions

Optimizers in TensorFlow

Reading from CSV files and preprocessing data

How to do it…

House price estimation-simple linear regression

House price estimation-multiple linear regression

Logistic regression on the MNIST dataset

Chapter 3: Neural Networks - Perceptron

Introduction.

Activation functions

Single layer perceptron

Calculating gradients of backpropagation algorithm

MNIST classifier using MLP

Function approximation using MLP-predicting Boston house prices

Tuning hyperparameters

Higher-level APIs-Keras

Chapter 4: Convolutional Neural Networks

Local receptive fields

Shared weights and bias

A mathematical example

ConvNets in TensorFlow

Pooling layers

Max pooling

Average pooling

ConvNets summary

Creating a ConvNet to classify handwritten MNIST numbers

Creating a ConvNet to classify CIFAR-10

Transferring style with VGG19 for image repainting

Using a pretrained VGG16 net for transfer learning

Creating a DeepDream network

Chapter 5: Advanced Convolutional Neural Networks

Creating a ConvNet for Sentiment Analysis

There is more...

Inspecting what filters a VGG pre-built network has learned

There is more.

Classifying images with VGGNet, ResNet, Inception, and Xception

VGG16 and VGG19

ResNet

Inception

Xception

Recycling pre-built Deep Learning models for extracting features

Very deep InceptionV3 Net used for Transfer Learning

Generating music with dilated ConvNets, WaveNet, and NSynth

Answering questions about images (Visual Q&amp

A)

Classifying videos with pre-trained nets in six different ways

Chapter 6: Recurrent Neural Networks

Vanishing and exploding gradients

Long Short Term Memory (LSTM)

Gated Recurrent Units (GRUs) and Peephole LSTM

Operating on sequences of vectors

Neural machine translation - training a seq2seq RNN

Neural machine translation - inference on a seq2seq RNN

All you need is attention - another example of a seq2seq RNN

Learning to write as Shakespeare with RNNs

First iteration

After a few iterations

Learning to predict future Bitcoin value with RNNs

Many-to-one and many-to-many RNN examples

Chapter 7: Unsupervised Learning

Principal component analysis

See also.

k-means clustering

Self-organizing maps

Restricted Boltzmann Machine

Recommender system using RBM

DBN for Emotion Detection

Chapter 8: Autoencoders

See Also

Vanilla autoencoders

Sparse autoencoder

Getting Ready...

There's More...

Denoising autoencoder

Getting Ready

Convolutional autoencoders

How it Works...

Stacked autoencoder

Chapter 9: Reinforcement Learning

Learning OpenAI Gym

Implementing neural network agent to play Pac-Man

Q learning to balance Cart-Pole

Game of Atari using Deep Q Networks

Policy gradients to play the game of Pong

AlphaGo Zero

Chapter 10: Mobile Computation

TensorFlow, mobile, and the cloud

Installing TensorFlow mobile for macOS and Android

There's more.

Playing with TensorFlow and Android examples

Installing TensorFlow mobile for macOS and iPhone

Optimizing a TensorFlow graph for mobile devices

Profiling a TensorFlow graph for mobile devices

Transforming a TensorFlow graph for mobile devices

Chapter 11: Generative Models and CapsNet

So what is a GAN?

Some cool GAN applications

Learning to forge MNIST images with simple GANs

Learning to forge MNIST images with DCGANs

Learning to forge Celebrity Faces and other datasets with DCGAN

Implementing Variational Autoencoders

Getting ready...

See also...

Learning to beat the previous MNIST state-of-the-art results with Capsule Networks

Chapter 12: Distributed TensorFlow and Cloud Deep Learning

Working with TensorFlow and GPUs

Playing with Distributed TensorFlow: multiple GPUs and one CPU

Playing with Distributed TensorFlow: multiple servers

Training a Distributed TensorFlow MNIST classifier

Working with TensorFlow Serving and Docker.

Getting ready.

Notes:

Includes index.

Description based on online resource; title from PDF title page (EBC, viewed January 11, 2018).

ISBN:

9781788291866

1788291867

OCLC:

1020288466