Book Overview & Buying
Table Of Contents
Feedback & Rating

TensorFlow 1.x Deep Learning Cookbook

3.4 (16)

Buy this Book

TensorFlow 1.x Deep Learning Cookbook

3.4 (16)

Buy this Book

Overview of this book

Deep neural networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. This exciting recipe-based guide will take you from the realm of DNN theory to implementing them practically to solve real-life problems in the 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-learning Networks (DQNs), and Generative Adversarial Networks (GANs), with easy-to-follow standalone recipes. You will learn how to use TensorFlow with Keras as the backend. You will learn how different DNNs perform on some popularly used datasets, such as MNIST, CIFAR-10, and Youtube8m. You will not only learn about the different mobile and embedded platforms supported by TensorFlow, but also how to set up cloud platforms for deep learning applications. You will also get a sneak peek at TPU architecture and how it will affect the future of DNNs. By using crisp, no-nonsense recipes, you will become an expert in implementing deep learning techniques in growing real-world applications and research areas such as reinforcement learning, GANs, and autoencoders.

Preface

What this book covers

What you need for this book

Who this book is for

Sections

Conventions

Reader feedback

Customer support

Free Chapter

TensorFlow - An Introduction

Introduction

Installing TensorFlow

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

Different Python packages required for DNN-based problems

Regression

Introduction

Choosing loss functions

Optimizers in TensorFlow

Reading from CSV files and preprocessing data

House price estimation-simple linear regression

House price estimation-multiple linear regression

Logistic regression on the MNIST dataset

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

See also

Convolutional Neural Networks

Introduction

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

Advanced Convolutional Neural Networks

Introduction

Creating a ConvNet for Sentiment Analysis

Inspecting what filters a VGG pre-built network has learned

Classifying images with VGGNet, ResNet, Inception, and 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&A)

Classifying videos with pre-trained nets in six different ways

Recurrent Neural Networks

Introduction

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

Learning to predict future Bitcoin value with RNNs

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

Unsupervised Learning

Introduction

Principal component analysis

k-means clustering

Self-organizing maps

Restricted Boltzmann Machine

Recommender system using RBM

DBN for Emotion Detection

Autoencoders

Introduction

Vanilla autoencoders

Sparse autoencoder

Denoising autoencoder

Convolutional autoencoders

Stacked autoencoder

Reinforcement Learning

Introduction

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

Mobile Computation

Introduction

Installing TensorFlow mobile for macOS and Android

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

Generative Models and CapsNet

Introduction

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

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

Distributed TensorFlow and Cloud Deep Learning

Introduction

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

Running Distributed TensorFlow on Google Cloud (GCP) with Compute Engine

Running Distributed TensorFlow on Google CloudML

Running Distributed TensorFlow on Microsoft Azure

Running Distributed TensorFlow on Amazon AWS

Learning to Learn with AutoML (Meta-Learning)

Meta-learning with recurrent networks and with reinforcement learning

Meta-learning blocks

Meta-learning novel tasks

Siamese Network

TensorFlow Processing Units

Components of TPUs

Customer Reviews

3.4 (16)

5 star

50%

4 star

6.3%

3 star

12.5%

2 star

1 star

31.3%

Sparse autoencoder

The autoencoder that we saw in the previous recipe worked more like an identity network--they simply reconstruct the input. The emphasis is to reconstruct the image at the pixel level, and the only constraint is the number of units in the bottleneck layer; while it is interesting, pixel-level reconstruction does not ensure that the network will learn abstract features from the dataset. We can ensure that the network learns abstract features from the dataset by adding further constraints.

In sparse autoencoders, a sparse penalty term is added to the reconstruction error, which tries to ensure that fewer units in the bottleneck layer will fire at any given time. If m is the total number of input patterns, then we can define a quantity ρ_hat (you can check the mathematical details in Andrew Ng's Lecture at https://web.stanford.edu/class/cs294a/sparseAutoencoder_2011new...