Deep Learning with TensorFlow

Giancarlo Zaccone, Md. Rezaul Karim

• Publisher: Packt Publishing
• Year: 2018
• eBook ISBN: 9781788831833
• Edition: 2
• Topic: Computer Science
• Subtopic: Application Development
• Index: Computer Science

Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of TensorFlow.

Key Features

• Learn how to implement advanced techniques in deep learning with Google's brainchild, TensorFlow
• Explore deep neural networks and layers of data abstraction with the help of this comprehensive guide
• Gain real-world contextualization through some deep learning problems concerning research and application

Book Description

Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks.

This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries.

Throughout the book, you'll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way.

You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects.

What you will learn

• Apply deep machine intelligence and GPU computing with TensorFlow
• Access public datasets and use TensorFlow to load, process, and transform the data
• Discover how to use the high-level TensorFlow API to build more powerful applications
• Use deep learning for scalable object detection and mobile computing
• Train machines quickly to learn from data by exploring reinforcement learning techniques
• Explore active areas of deep learning research and applications

Who this book is for

The book is for people interested in machine learning and machine intelligence. A rudimentary level of programming in one language is assumed, as is a basic familiarity with computer science techniques and technologies, including a basic awareness of computer hardware and algorithms. Some competence in mathematics is needed to the level of elementary linear algebra and calculus.

Deep Learning with TensorFlow - Second Edition

---

Table of Contents

Deep Learning with TensorFlow - Second Edition

Why subscribe?

PacktPub.com

Contributors

About the authors

About the reviewers

Packt is Searching for Authors Like You

Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

Download the color images

Conventions used

Get in touch

Reviews

1. Getting Started with Deep Learning

A soft introduction to machine learning

Supervised learning

Unbalanced data

Unsupervised learning

Reinforcement learning

What is deep learning?

Artificial neural networks

The biological neurons

The artificial neuron

How does an ANN learn?

ANNs and the backpropagation algorithm

Weight optimization

Stochastic gradient descent

Neural network architectures

Deep Neural Networks (DNNs)

Multilayer perceptron

Deep Belief Networks (DBNs)

Convolutional Neural Networks (CNNs)

AutoEncoders

Recurrent Neural Networks (RNNs)

Emergent architectures

Deep learning frameworks

Summary

2. A First Look at TensorFlow

A general overview of TensorFlow

What's new from TensorFlow v1.6 forwards?

Nvidia GPU support optimized

Introducing TensorFlow Lite

Eager execution

Optimized Accelerated Linear Algebra (XLA)

Installing and configuring TensorFlow

TensorFlow computational graph

TensorFlow code structure

Eager execution with TensorFlow

Data model in TensorFlow

Tensor

Rank and shape

Data type

Variables

Fetches

Feeds and placeholders

Visualizing computations through TensorBoard

How does TensorBoard work?

Linear regression and beyond

Linear regression revisited for a real dataset

Summary

3. Feed-Forward Neural Networks with TensorFlow

Feed-forward neural networks (FFNNs)

Feed-forward and backpropagation

Weights and biases

Activation functions

Using sigmoid

Using tanh

Using ReLU

Using softmax

Implementing a feed-forward neural network

Exploring the MNIST dataset

Softmax classifier

Implementing a multilayer perceptron (MLP)

Training an MLP

Using MLPs

Dataset description

Preprocessing

A TensorFlow implementation of MLP for client-subscription assessment

Deep Belief Networks (DBNs)

Restricted Boltzmann Machines (RBMs)

Construction of a simple DBN

Unsupervised pre-training

Supervised fine-tuning

Implementing a DBN with TensorFlow for client-subscription assessment

Tuning hyperparameters and advanced FFNNs

Tuning FFNN hyperparameters

Number of hidden layers

Number of neurons per hidden layer

Weight and biases initialization

Selecting the most suitable optimizer

GridSearch and randomized search for hyperparameters tuning

Regularization

Dropout optimization

Summary

4. Convolutional Neural Networks

Main concepts of CNNs

CNNs in action

LeNet5

Implementing a LeNet-5 step by step

AlexNet

Transfer learning

Pretrained AlexNet

Dataset preparation

Fine-tuning implementation

VGG

Artistic style learning with VGG-19

Input images

Content extractor and loss

Style extractor and loss

Merger and total loss

Training

Inception-v3

Exploring Inception with TensorFlow

Emotion recognition with CNNs

Testing the model on your own image

Source code

Summary

5. Optimizing TensorFlow Autoencoders

How does an autoencoder work?

Implementing autoencoders with TensorFlow

Improving autoencoder robustness

Implementing a denoising autoencoder

Implementing a convolutional autoencoder

Encoder

Decoder

Fraud analytics with autoencoders

Description of the dataset

Problem description

Exploratory data analysis

Training, validation, and testing set preparation

Normalization

Autoencoder as an unsupervised feature learning algorithm

Evaluating the model

Summary

6. Recurrent Neural Networks

Working principles of RNNs

Implementing basic RNNs in TensorFlow

RNN and the long-term dependency problem

Bi-directional RNNs

RNN and the gradient vanishing-exploding problem

LSTM networks

GRU cell

Implementing an RNN for spam prediction

Data description and preprocessing

Developing a predictive model for time series data

Description of the dataset

Pre-processing and exploratory analysis

LSTM predictive model

Model evaluation

An LSTM predictive model for sentiment analysis

Network design

LSTM model training

Visualizing through TensorBoard

LSTM model evaluation

Human activity recognition using LSTM model

Dataset description

Workflow of the LSTM model for HAR

Implementing an LSTM model for HAR

Summary

7. Heterogeneous and Distributed Computing

GPGPU computing

The GPGPU history

The CUDA architecture

The GPU programming model

The TensorFlow GPU setup

Update TensorFlow

GPU representation

Using a GPU

GPU memory management

Assigning a single GPU on a multi-GPU system

The source code for GPU with soft placement

Using multiple GPUs

Distributed computing

Model parallelism

Data parallelism

The distributed TensorFlow setup

Summary

8. Advanced TensorFlow Programming

tf.estimator

Estimators

Graph actions

Parsing resources

Flower predictions

TFLearn

Installation

Titanic survival predictor

PrettyTensor

Chaining layers

Normal mode

Sequential mode

Branch and join

Digit classifier

Keras

Keras programming models

Sequential model

Sentiment classification of movie reviews

Functional API

SqueezeNet

Summary

9. Recommendation Systems Using Factorization Machines

Recommendation systems

Collaborative filtering approaches

Content-based filtering approaches

Hybrid recommender systems

Model-based collaborative filtering

Movie recommendation using collaborative filtering

The utility matrix

Description of the dataset

Ratings data

Movies data

Users data

Exploratory analysis of the MovieLens dataset

Implementing a movie RE

Training the model with the available ratings

Inferencing the saved model

Generating the user-item table

Clustering similar movies

Movie rating prediction by users

Finding top k movies

Predicting top k similar movies

Computing user-user similarity

Evaluating the recommender system

Factorization machines for recommendation...

Table of contents

• Deep Learning with TensorFlow - Second Edition