Artificial Intelligence Engines

A Tutorial Introduction to the Mathematics of Deep Learning

James V Stone

Preface

List of Pseudocode Examples

Online Code Examples

1 Artificial Neural Networks

1.1 Introduction

1.2 What is an Artificial Neural Network?

1.3 The Origins of Neural Networks

1.4 From Backprop to Deep Learning

1.5 An Overview of Chapters

2 Linear Associative Networks

2.1 Introduction

2.2 Setting One Connection Weight

2.3 Learning One Association

2.4 Gradient Descent

2.5 Learning Two Associations

2.6 Learning Many Associations

2.7 Learning Photographs

2.8 Summary

3 Perceptrons

3.1 Introduction

3.2 The Perceptron Learning Algorithm

3.3 The Exclusive OR Problem

3.4 Why Exclusive OR Matters

3.5. Summary

4 The Backpropagation Algorithm

4.1 Introduction

4.2 The Backpropagation Algorithm

4.3 Why Use Sigmoidal Hidden Units?

4.4 Generalisation and Overfitting

4.5 Vanishing Gradients

4.6 Speeding Up Backprop

4.7 Local and Global Mimima

4.8 Temporal Backprop

4.9 Early Backprop Achievements

4.10 Summary

5 Hopfield Nets

5.1 Introduction

5.2 The Hopfield Net

5.3 Learning One Network State

5.4 Content Addressable Memory

5.5 Tolerance to Damage

5.6 The Energy Function

5.7 Summary

6 Boltzmann Machines

6.1 Introduction

6.2 Learning in Generative Models

6.3 The Boltzmann Machine Energy Function

6.4 Simulated Annealing

6.5 Learning by Sculpting Distributions

6.6 Learning in Boltzmann Machines

6.7 Learning by Maximising Likelihood

6.8 Autoencoder Networks

6.9 Summary

7 Deep RBMs

7.1 Introduction

7.2 Restricted Boltzmann Machines

7.3 Training Restricted Boltzmann Machines

7.4 Deep Autoencoder Networks

7.5 Summary

8 Variational Autoencoders

8.1 Introduction

8.2 Why Favour Independent Features?

8.3 Overview of Variational Autoencoders

8.4 Latent Variables and Manifolds

8.5 Key Quantities

8.6 How Variational Autoencoders Work

8.7 The Evidence Lower Bound

8.8 An Alternative Derivation

8.9 Maximising the Lower Bound

8.10 Conditional Variational Autoencoders

8.11 Summary

9 Deep Backprop Networks

9.1 Introduction

9.2 Convolutional Neural Networks

9.3 LeNet1

9.4 LeNet5

9.5 AlexNet

9.6 GoogLeNet

9.7 ResNet

9.8 Ladder Autoencoder Networks

9.9 Denoising Autoencoder Networks

9.10 Fooling Neural Networks

9.11 Generative Adversarial Networks

9.12 Temporal Deep Neural Networks

9.13 Capsule Neural Networks

9.14 Summary

10 Reinforcement Learning

10.1 Introduction

10.2 What's the Problem?

10.3 Key Quantities

10.4 Markov Decision Processes

10.5 Formalising the Problem

10.6 The Bellman Equation

10.7 Learning State-Value Functions

10.8 Eligibility Traces

10.9 Learning Action-Value Functions

10.10 Balancing a Pole

10.11 Applications

10.12 Summary

11 The Emperor’s New AI?

11.1 Artificial Intelligence

11.2 Yet Another Revolution?

Further Reading

Appendices

A Glossary

B Mathematical Symbols

C A Vector Matrix Tutorial

D Maximum Likelihood Estimation

E Bayes’ Theorem

References

Index

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