Book Image

Deep Learning By Example

Book Image

Deep Learning By Example

Overview of this book

Deep learning is a popular subset of machine learning, and it allows you to build complex models that are faster and give more accurate predictions. This book is your companion to take your first steps into the world of deep learning, with hands-on examples to boost your understanding of the topic. This book starts with a quick overview of the essential concepts of data science and machine learning which are required to get started with deep learning. It introduces you to Tensorflow, the most widely used machine learning library for training deep learning models. You will then work on your first deep learning problem by training a deep feed-forward neural network for digit classification, and move on to tackle other real-world problems in computer vision, language processing, sentiment analysis, and more. Advanced deep learning models such as generative adversarial networks and their applications are also covered in this book. By the end of this book, you will have a solid understanding of all the essential concepts in deep learning. With the help of the examples and code provided in this book, you will be equipped to train your own deep learning models with more confidence.

Preface

Who this book is for

What this book covers

To get the most out of this book

Free Chapter

Data Science - A Birds' Eye View

Data Science - A Birds' Eye View

Understanding data science by an example

Design procedure of data science algorithms

Getting to learn

Implementing the fish recognition/detection model

Different learning types

Data size and industry needs

Data Modeling in Action - The Titanic Example

Data Modeling in Action - The Titanic Example

Linear models for regression

Linear models for classification

Titanic example – model building and training

Different types of errors

Apparent (training set) error

Generalization/true error

Feature Engineering and Model Complexity – The Titanic Example Revisited

Feature Engineering and Model Complexity – The Titanic Example Revisited

Feature engineering

The curse of dimensionality

Titanic example revisited – all together

Bias-variance decomposition

Learning visibility

Get Up and Running with TensorFlow

Get Up and Running with TensorFlow

TensorFlow installation

The TensorFlow environment

Computational graphs

TensorFlow data types, variables, and placeholders

Getting output from TensorFlow

TensorBoard – visualizing learning

TensorFlow in Action - Some Basic Examples

TensorFlow in Action - Some Basic Examples

Capacity of a single neuron

Activation functions

Feed-forward neural network

The need for multilayer networks

TensorFlow terminologies – recap

Linear regression model – building and training

Logistic regression model – building and training

Deep Feed-forward Neural Networks - Implementing Digit Classification

Deep Feed-forward Neural Networks - Implementing Digit Classification

Hidden units and architecture design

MNIST dataset analysis

Digit classification – model building and training

Introduction to Convolutional Neural Networks

Introduction to Convolutional Neural Networks

The convolution operation

Different layers of CNNs

CNN basic example – MNIST digit classification

Object Detection – CIFAR-10 Example

Object Detection – CIFAR-10 Example

Object detection

CIFAR-10 – modeling, building, and training

Object Detection – Transfer Learning with CNNs

Object Detection – Transfer Learning with CNNs

Transfer learning

CIFAR-10 object detection – revisited

Recurrent-Type Neural Networks - Language Modeling

Recurrent-Type Neural Networks - Language Modeling

The intuition behind RNNs

Implementation of the language model

Representation Learning - Implementing Word Embeddings

Representation Learning - Implementing Word Embeddings

Introduction to representation learning

A practical example of the skip-gram architecture

Skip-gram Word2Vec implementation

Neural Sentiment Analysis

Neural Sentiment Analysis

General sentiment analysis architecture

Sentiment analysis – model implementation

Autoencoders – Feature Extraction and Denoising

Autoencoders – Feature Extraction and Denoising

Introduction to autoencoders

Examples of autoencoders

Autoencoder architectures

Compressing the MNIST dataset

Convolutional autoencoder

Denoising autoencoders

Applications of autoencoders

Generative Adversarial Networks

Generative Adversarial Networks

An intuitive introduction

Simple implementation of GANs

Face Generation and Handling Missing Labels

Face Generation and Handling Missing Labels

Face generation

Semi-supervised learning with Generative Adversarial Networks (GANs)

Implementing Fish Recognition

Implementing Fish Recognition

Code for fish recognition

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Examples of autoencoders

In this chapter, we will demonstrate some examples of different variations of autoencoders using the MNIST dataset. As a concrete example, suppose the inputs x are the pixel intensity values from a 28 x 28 image (784 pixels); so the number of input data samples is n=784. There are s2=392 hidden units in layer L2. And since the output will be of the same dimensions as the input data samples, y ∈ R784. The number of neurons in the input layer will be 784, followed by 392 neurons in the middle layer L2; so the network will be a lower representation, which is a compressed version of the output. The network will then feed this compressed lower representation of the input a(L2) ∈ R392 to the second part of the network, which will try hard to reconstruct the input pixels 784 from this compressed version.

Autoencoders rely on the fact that the input...