Python Deep Learning - Second Edition by Zocca, Valentino -- Read -- Imperial Library of Trantor

Index

Title Page Copyright and Credits

Python Deep Learning Second Edition

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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 Download the color images Conventions used

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Machine Learning - an Introduction

Introduction to machine learning Different machine learning approaches

Supervised learning

Linear and logistic regression Support vector machines Decision Trees Naive Bayes

Unsupervised learning

K-means

Reinforcement learning

Q-learning

Components of an ML solution

Neural networks

Introduction to PyTorch

Summary

Neural Networks

The need for neural networks An introduction to neural networks

An introduction to neurons An introduction to layers Multi-layer neural networks Different types of activation function Putting it all together with an example

Training neural networks 

Linear regression Logistic regression Backpropagation Code example of a neural network for the XOR function 

Summary

Deep Learning Fundamentals

Introduction to deep learning Fundamental deep learning concepts 

Feature learning

Deep learning algorithms

Deep networks

A brief history of contemporary deep learning

Training deep networks

Applications of deep learning The reasons for deep learning's popularity Introducing popular open source libraries

TensorFlow Keras PyTorch Using Keras to classify handwritten digits Using Keras to classify images of objects

Summary

Computer Vision with Convolutional Networks

Intuition and justification for CNN Convolutional layers

A coding example of convolution operation

Stride and padding in convolutional layers

1D, 2D, and 3D convolutions 1x1 convolutions Backpropagation in convolutional layers Convolutional layers in deep learning libraries

Pooling layers The structure of a convolutional network

Classifying handwritten digits with a convolutional network 

Improving the performance of CNNs

Data pre-processing Regularization Weight decay Dropout Data augmentation Batch normalization

A CNN example with Keras and CIFAR-10 Summary

Advanced Computer Vision

Transfer learning

Transfer learning example with PyTorch

Advanced network architectures

VGG

VGG with Keras, PyTorch, and TensorFlow

Residual networks Inception networks

Inception v1 Inception v2 and v3 Inception v4 and Inception-ResNet Xception and MobileNets

DenseNets

Capsule networks

Limitations of convolutional networks Capsules

Dynamic routing

Structure of the capsule network

Advanced computer vision tasks

Object detection

Approaches to object detection Object detection with YOLOv3 A code example of YOLOv3 with OpenCV

Semantic segmentation

Artistic style transfer Summary

Generating Images with GANs and VAEs

Intuition and justification of generative models Variational autoencoders

Generating new MNIST digits with VAE

Generative Adversarial networks

Training GANs

Training the discriminator Training the generator Putting it all together

Types of GANs

DCGAN

The generator in DCGAN

Conditional GANs

Generating new MNIST images with GANs and Keras

Summary

Recurrent Neural Networks and Language Models

Recurrent neural networks

RNN implementation and training

Backpropagation through time Vanishing and exploding gradients

Long short-term memory Gated recurrent units

Language modeling

Word-based models

N-grams Neural language models

Neural probabilistic language model word2vec Visualizing word embedding vectors

Character-based models for generating new text

Preprocessing and reading data LSTM network Training Sampling Example training

Sequence to sequence learning

Sequence to sequence with attention

Speech recognition

Speech recognition pipeline Speech as input data Preprocessing Acoustic model

Recurrent neural networks CTC

Decoding End-to-end models

Summary

Reinforcement Learning Theory

RL paradigms

Differences between RL and other ML approaches Types of RL algorithms

Types of RL agents

RL as a Markov decision process

Bellman equations Optimal policies and value functions

Finding optimal policies with Dynamic Programming

Policy evaluation

Policy evaluation example

Policy improvements Policy and value iterations

Monte Carlo methods

Policy evaluation Exploring starts policy improvement Epsilon-greedy policy improvement

Temporal difference methods

Policy evaluation Control with Sarsa Control with Q-learning Double Q-learning

Value function approximations

Value approximation for Sarsa and Q-learning

Improving the performance of Q-learning

Fixed target Q-network

Experience replay Q-learning in action Summary

Deep Reinforcement Learning for Games

Introduction to genetic algorithms playing games Deep Q-learning

Playing Atari Breakout with Deep Q-learning

Policy gradient methods

Monte Carlo policy gradients with REINFORCE Policy gradients with actor–critic

Actor-Critic with advantage

Playing cart pole with A2C

Model-based methods

Monte Carlo Tree Search Playing board games with AlphaZero

Summary

Deep Learning in Autonomous Vehicles

Brief history of AV research AV introduction

Components of an AV system 

Sensors

Deep learning and sensors

Vehicle localization Planning

Imitiation driving policy

Behavioral cloning with PyTorch

Driving policy with ChauffeurNet

Model inputs and outputs Model architecture Training

DL in the Cloud Summary

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