Deep Learning by Example by Menshawy, Ahmed -- Read -- Imperial Library of Trantor

Index

Title Page Copyright and Credits

Deep Learning By Example

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Contributors

About the author 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

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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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