Deep Learning By Example电子书

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Copyright and Credits

Deep Learning By Example

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Contributors

About the author

About the reviewers

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Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

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Conventions used

Get in touch

Reviews

Data Science - A Birds' Eye View

Understanding data science by an example

Design procedure of data science algorithms

Data pre-processing

Data cleaning

Data pre-processing

Feature selection

Model selection

Learning process

Evaluating your model

Getting to learn

Challenges of learning

Feature extraction – feature engineering

Noise

Overfitting

Selection of a machine learning algorithm

Prior knowledge

Missing values

Implementing the fish recognition/detection model

Knowledge base/dataset

Data analysis pre-processing

Model building

Model training and testing

Fish recognition – all together

Different learning types

Supervised learning

Unsupervised learning

Semi-supervised learning

Reinforcement learning

Data size and industry needs

Summary

Data Modeling in Action - The Titanic Example

Linear models for regression

Motivation

Advertising – a financial example

Dependencies

Importing data with pandas

Understanding the advertising data

Data analysis and visualization

Simple regression model

Learning model coefficients

Interpreting model coefficients

Using the model for prediction

Linear models for classification

Classification and logistic regression

Titanic example – model building and training

Data handling and visualization

Data analysis – supervised machine learning

Different types of errors

Apparent (training set) error

Generalization/true error

Summary

Feature Engineering and Model Complexity – The Titanic Example Revisited

Feature engineering

Types of feature engineering

Feature selection

Dimensionality reduction

Feature construction

Titanic example revisited

Missing values

Removing any sample with missing values in it

Missing value inputting

Assigning an average value

Using a regression or another simple model to predict the values of missing variables

Feature transformations

Dummy features

Factorizing

Scaling

Binning

Derived features

Name

Cabin

Ticket

Interaction features

The curse of dimensionality

Avoiding the curse of dimensionality

Titanic example revisited – all together

Bias-variance decomposition

Learning visibility

Breaking the rule of thumb

Summary

Get Up and Running with TensorFlow

TensorFlow installation

TensorFlow GPU installation for Ubuntu 16.04

Installing NVIDIA drivers and CUDA 8

Installing TensorFlow

TensorFlow CPU installation for Ubuntu 16.04

TensorFlow CPU installation for macOS X

TensorFlow GPU/CPU installation for Windows

The TensorFlow environment

Computational graphs

TensorFlow data types, variables, and placeholders

Variables

Placeholders

Mathematical operations

Getting output from TensorFlow

TensorBoard – visualizing learning

Summary

TensorFlow in Action - Some Basic Examples

Capacity of a single neuron

Biological motivation and connections

Activation functions

Sigmoid

Tanh

ReLU

Feed-forward neural network

The need for multilayer networks

Training our MLP – the backpropagation algorithm

Step 1 – forward propagation

Step 2 – backpropagation and weight updation

TensorFlow terminologies – recap

Defining multidimensional arrays using TensorFlow

Why tensors?

Variables

Placeholders

Operations

Linear regression model – building and training

Linear regression with TensorFlow

Logistic regression model – building and training

Utilizing logistic regression in TensorFlow

Why use placeholders?

Set model weights and bias

Logistic regression model

Training

Cost function

Summary

Deep Feed-forward Neural Networks - Implementing Digit Classification

Hidden units and architecture design

MNIST dataset analysis

The MNIST data

Digit classification – model building and training

Data analysis

Building the model

Model training

Summary

Introduction to Convolutional Neural Networks

The convolution operation

Motivation

Applications of CNNs

Different layers of CNNs

Input layer

Convolution step

Introducing non-linearity

The pooling step

Fully connected layer

Logits layer

CNN basic example – MNIST digit classification

Building the model

Cost function

Performance measures

Model training

Summary

Object Detection – CIFAR-10 Example

Object detection

CIFAR-10 – modeling, building, and training

Used packages

Loading the CIFAR-10 dataset

Data analysis and preprocessing

Building the network

Model training

Testing the model

Summary

Object Detection – Transfer Learning with CNNs

Transfer learning

The intuition behind TL

Differences between traditional machine learning and TL

CIFAR-10 object detection – revisited

Solution outline

Loading and exploring CIFAR-10

Inception model transfer values

Analysis of transfer values

Model building and training

Summary

Recurrent-Type Neural Networks - Language Modeling

The intuition behind RNNs

Recurrent neural networks architectures

Examples of RNNs

Character-level language models

Language model using Shakespeare data

The vanishing gradient problem

The problem of long-term dependencies

LSTM networks

Why does LSTM work?

Implementation of the language model

Mini-batch generation for training

Building the model

Stacked LSTMs

Model architecture

Inputs

Building an LSTM cell

RNN output

Training loss

Optimizer

Building the network

Model hyperparameters

Training the model

Saving checkpoints

Generating text

Summary

Representation Learning - Implementing Word Embeddings

Introduction to representation learning

Word2Vec

Building Word2Vec model

A practical example of the skip-gram architecture

Skip-gram Word2Vec implementation

Data analysis and pre-processing

Building the model

Training

Summary

Neural Sentiment Analysis

General sentiment analysis architecture

RNNs – sentiment analysis context

Exploding and vanishing gradients - recap

Sentiment analysis – model implementation

Keras

Data analysis and preprocessing

Building the model

Model training and results analysis

Summary

Autoencoders – Feature Extraction and Denoising

Introduction to autoencoders

Examples of autoencoders

Autoencoder architectures

Compressing the MNIST dataset

The MNIST dataset

Building the model

Model training

Convolutional autoencoder

Dataset

Building the model

Model training

Denoising autoencoders

Building the model

Model training

Applications of autoencoders

Image colorization

More applications

Summary

Generative Adversarial Networks

An intuitive introduction

Simple implementation of GANs

Model inputs

Variable scope

Leaky ReLU

Generator

Discriminator

Building the GAN network

Model hyperparameters

Defining the generator and discriminator

Discriminator and generator losses

Optimizers

Model training

Generator samples from training

Sampling from the generator

Summary

Face Generation and Handling Missing Labels

Face generation

Getting the data

Exploring the Data

Building the model

Model inputs

Discriminator

Generator

Model losses

Model optimizer

Training the model

Semi-supervised learning with Generative Adversarial Networks (GANs)

Intuition

Data analysis and preprocessing

Building the model

Model inputs

Generator

Discriminator

Model losses

Model optimizer

Model training

Summary

Implementing Fish Recognition

Code for fish recognition

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