Artificial Intelligence Engines
A Tutorial Introduction to the Mathematics of Deep Learning
Published on 3. February 2025
217 pages
978-1-83702-406-3 (ISBN)
Description
No detailed description available for "Artificial Intelligence Engines".
More details
Edition
Digital original
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
US School Grade: College Graduate Student
Edition type
Digital original
File size
13,99 MB
ISBN-13
978-1-83702-406-3 (9781837024063)
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Schweitzer Classification
Person
Stone James V :
James V Stone is a distinguished academic and author specializing in computational neuroscience, artificial intelligence, and information theory. He earned a BSc in Psychology and Pharmacology from Manchester University, an MSc in Knowledge-Based Systems, and a DPhil in Computer Vision from Sussex University. A former Wellcome Mathematical Biology Research Fellow and Associate Professor at the University of Sheffield, James has investigated topics like brain evolution, quantum mechanics, and the Baldwin effect. Since 2017, he has focused on making complex scientific ideas accessible through compelling writing.
James V Stone is a distinguished academic and author specializing in computational neuroscience, artificial intelligence, and information theory. He earned a BSc in Psychology and Pharmacology from Manchester University, an MSc in Knowledge-Based Systems, and a DPhil in Computer Vision from Sussex University. A former Wellcome Mathematical Biology Research Fellow and Associate Professor at the University of Sheffield, James has investigated topics like brain evolution, quantum mechanics, and the Baldwin effect. Since 2017, he has focused on making complex scientific ideas accessible through compelling writing.
Content
- Intro
- List of Pseudocode Examples
- Online Code Examples
- Preface
- Artificial Neural Networks
- Introduction
- What is an Artificial Neural Network?
- The Origins of Neural Networks
- From Backprop to Deep Learning
- An Overview of Chapters
- Linear Associative Networks
- Introduction
- Setting One Connection Weight
- Learning One Association
- Gradient Descent
- Learning Two Associations
- Learning Many Associations
- Learning Photographs
- Summary
- Perceptrons
- Introduction
- The Perceptron Learning Algorithm
- The Exclusive OR Problem
- Why Exclusive OR Matters
- Summary
- The Backpropagation Algorithm
- Introduction
- The Backpropagation Algorithm
- Why Use Sigmoidal Hidden Units?
- Generalisation and Over-fitting
- Vanishing Gradients
- Speeding Up Backprop
- Local and Global Mimima
- Temporal Backprop
- Early Backprop Achievements
- Summary
- Hopfield Nets
- Introduction
- The Hopfield Net
- Learning One Network State
- Content Addressable Memory
- Tolerance to Damage
- The Energy Function
- Summary
- Boltzmann Machines
- Introduction
- Learning in Generative Models
- The Boltzmann Machine Energy Function
- Simulated Annealing
- Learning by Sculpting Distributions
- Learning in Boltzmann Machines
- Learning by Maximising Likelihood
- Autoencoder Networks
- Summary
- Deep RBMs
- Introduction
- Restricted Boltzmann Machines
- Training Restricted Boltzmann Machines
- Deep Autoencoder Networks
- Summary
- Variational Autoencoders
- Introduction
- Why Favour Independent Features?
- Overview of Variational Autoencoders
- Latent Variables and Manifolds
- Key Quantities
- How Variational Autoencoders Work
- The Evidence Lower Bound
- An Alternative Derivation
- Maximising the Lower Bound
- Conditional Variational Autoencoders
- Applications
- Summary
- Deep Backprop Networks
- Introduction
- Convolutional Neural Networks
- LeNet1
- LeNet5
- AlexNet
- GoogLeNet
- ResNet
- Ladder Autoencoder Networks
- Denoising Autoencoders
- Fooling Neural Networks
- Generative Adversarial Networks
- Temporal Deep Neural Networks
- Capsule Networks
- Summary
- Reinforcement Learning
- Introduction
- What's the Problem?
- Key Quantities
- Markov Decision Processes
- Formalising the Problem
- The Bellman Equation
- Learning State-Value Functions
- Eligibility Traces
- Learning Action-Value Functions
- Balancing a Pole
- Applications
- Summary
- The Emperor's New AI?
- Artificial Intelligence
- Yet Another Revolution?
- Further Reading
- Appendix A - Glossary
- Appendix B - Mathematical Symbols
- Appendix C - A Vector and Matrix Tutorial
- Appendix D - Maximum Likelihood Estimation
- Appendix E - Bayes' Theorem
- References
- Index
- Appendices
