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Deep Learning with TensorFlow

By : Giancarlo Zaccone, Fabrizio Milo, Md. Rezaul Karim

2 (10)

Deep Learning with TensorFlow

2 (10)

By: Giancarlo Zaccone, Fabrizio Milo, Md. Rezaul Karim

Overview of this book

Deep learning is the step that comes after machine learning, and has more advanced implementations. Machine learning is not just for academics anymore, but is becoming a mainstream practice through wide adoption, and deep learning has taken the front seat. As a data scientist, if you want to explore data abstraction layers, this book will be your guide. This book shows how this can be exploited in the real world with complex raw data using TensorFlow 1.x. Throughout the book, you’ll learn how to implement deep learning algorithms for machine learning systems and integrate them into your product offerings, including search, image recognition, and language processing. Additionally, you’ll learn how to analyze and improve the performance of deep learning models. This can be done by comparing algorithms against benchmarks, along with machine intelligence, to learn from the information and determine ideal behaviors within a specific context. After finishing the book, you will be familiar with machine learning techniques, in particular the use of TensorFlow for deep learning, and will be ready to apply your knowledge to research or commercial projects.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

Getting Started with Deep Learning

Getting Started with Deep Learning

Introducing machine learning

What is deep learning?

Neural networks

How does an artificial neural network learn?

Neural network architectures

Autoencoders

Recurrent Neural Networks

Deep learning framework comparisons

Summary

First Look at TensorFlow

First Look at TensorFlow

General overview

Installing TensorFlow on Linux

Requirements for running TensorFlow with GPU from NVIDIA

How to install TensorFlow

Installing TensorFlow on Windows

Computational graphs

Why a computational graph?

The programming model

Data model

TensorBoard

Implementing a single input neuron

Source code for the single input neuron

Migrating to TensorFlow 1.x

Summary

Using TensorFlow on a Feed-Forward Neural Network

Using TensorFlow on a Feed-Forward Neural Network

Introducing feed-forward neural networks

Classification of handwritten digits

Exploring the MNIST dataset

Softmax classifier

How to save and restore a TensorFlow model

Implementing a five-layer neural network

ReLU classifier

Visualization

Dropout optimization

Visualization

Summary

TensorFlow on a Convolutional Neural Network

TensorFlow on a Convolutional Neural Network

Introducing CNNs

CNN architecture

Building your first CNN

Emotion recognition with CNNs

Summary

Optimizing TensorFlow Autoencoders

Optimizing TensorFlow Autoencoders

Introducing autoencoders

Implementing an autoencoder

Improving autoencoder robustness

Building a denoising autoencoder

Convolutional autoencoders

Summary

Recurrent Neural Networks

Recurrent Neural Networks

RNNs basic concepts

RNNs at work

Unfolding an RNN

The vanishing gradient problem

LSTM networks

An image classifier with RNNs

Bidirectional RNNs

Text prediction

Summary

GPU Computing

GPU Computing

GPGPU computing

GPGPU history

The CUDA architecture

GPU programming model

TensorFlow GPU set up

TensorFlow GPU management

GPU memory management

Assigning a single GPU on a multi-GPU system

Using multiple GPUs

Summary

Advanced TensorFlow Programming

Advanced TensorFlow Programming

Introducing Keras

Building deep learning models

Sentiment classification of movie reviews

Adding a convolutional layer

Pretty Tensor

Digit classifier

TFLearn

Titanic survival predictor

Summary

Advanced Multimedia Programming with TensorFlow

Advanced Multimedia Programming with TensorFlow

Introduction to multimedia analysis

Deep learning for Scalable Object Detection

Accelerated Linear Algebra

TensorFlow and Keras

Deep learning on Android

Summary

Reinforcement Learning

Reinforcement Learning

Basic concepts of Reinforcement Learning

Q-learning algorithm

Introducing the OpenAI Gym framework

FrozenLake-v0 implementation problem

Q-learning with TensorFlow

Source code for the Q-learning neural network

Summary

Computational graphs

When performing an operation, for example training a neural network, or the sum of two integers, TensorFlow internally represent, its computation using a data flow graph (or computational graph).

This is a directed graph consisting of the following:

A set of nodes, each one representing an operation
A set of directed arcs, each one representing the data on which the operations are performed

TensorFlow has two types of edge:

Normal: They are only carriers of data structures, between the nodes. The output of one operation (from one node) becomes the input for another operation. The edge connecting two nodes carry the values.
Special: This edge doesn't carry values. It represents a control dependency between two nodes A and B. It means that the node B will be executed only if the operation in A will be ended before the relationship between operations on the data.

The TensorFlow implementation...

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83

Tech Concepts

36

Programming languages

73

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