Machine Learning with Neural Networks An Introduction for Scientists and Engineers 1st edition by Bernhard Mehlig ISBN 1108494935 978-1108494939

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ISBN 10: 1108494935
ISBN 13:  978-1108494939
Author: Bernhard Mehlig

This modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. In addition to describing the mathematical principles of the topic, and its historical evolution, strong connections are drawn with underlying methods from statistical physics and current applications within science and engineering. Closely based around a well-established undergraduate course, this pedagogical text provides a solid understanding of the key aspects of modern machine learning with artificial neural networks, for students in physics, mathematics, and engineering. Numerous exercises expand and reinforce key concepts within the book and allow students to hone their programming skills. Frequent references to current research develop a detailed perspective on the state-of-the-art in machine learning research.

Machine Learning with Neural Networks: An Introduction for Scientists and Engineers 1st Table of contents:

1 Introduction

1.1 Neural Networks

1.2 McCulloch-Pitts Neurons

1.3 Activation Functions

1.4 Asynchronous Updates

1.5 Summary

1.6 Further Reading

Part I Hopfield Networks

2 Deterministic Hopfield Networks

2.1 Pattern Recognition

2.2 Hopfield Networks and Hebb’s Rule

2.3 The Cross-Talk Term

2.4 One-Step Error Probability

2.5 Energy Function

2.6 Summary

2.7 Exercises

3 Stochastic Hopfield Networks

3.1 Stochastic Dynamics

3.2 Order Parameters

3.3 Mean-Field Theory

3.4 Critical Storage Capacity

3.5 Beyond Mean-Field Theory

3.6 Correlated and Non-Random Patterns

3.7 Summary

3.8 Further Reading

3.9 Exercises

4 The Boltzmann Distribution

4.1 Convergence of the Stochastic Dynamics

4.2 Monte-Carlo Simulation

4.3 Simulated Annealing

4.4 Boltzmann Machines

4.5 Restricted Boltzmann Machines

4.6 Summary

4.7 Further Reading

4.8 Exercises

Part II Supervised Learning

5 Perceptrons

5.1 A Classification Problem

5.2 Iterative Learning Algorithm

5.3 Gradient Descent for Linear Units

5.4 Classification Capacity

5.5 Multilayer Perceptrons

5.6 Summary

5.7 Further Reading

5.8 Exercises

6 Stochastic Gradient Descent

6.1 Chain Rule and Error Backpropagation

6.2 Stochastic Gradient-Descent Algorithm

6.3 Preprocessing the Input Data

6.4 Overfitting and Cross Validation

6.5 Adaptation of the Learning Rate

6.6 Summary

6.7 Further Reading

6.8 Exercises

7 Deep Learning

7.1 How Many Hidden Layers?

7.2 Vanishing and Exploding Gradients

7.3 Rectified Linear Units

7.4 Residual Networks

7.5 Outputs and Energy Functions

7.6 Regularisation

7.7 Summary

7.8 Further Reading

7.9 Exercises

8 Convolutional Networks

8.1 Convolution Layers

8.2 Pooling Layers

8.3 Learning to Read Handwritten Digits

8.4 Coping with Deformations of the Input Distribution

8.5 Deep Learning for Object Recognition

8.6 Summary

8.7 Further Reading

8.8 Exercises

9 Supervised Recurrent Networks

9.1 Recurrent Backpropagation

9.2 Backpropagation through Time

9.3 Vanishing Gradients

9.4 Recurrent Networks for Machine Translation

9.5 Reservoir Computing

9.6 Summary

9.7 Further Reading

9.8 Exercises

Part III Learning without Labels

10 Unsupervised Learning

10.1 Oja’s Rule

10.2 Competitive Learning

10.3 Self-Organising Maps

10.4 K-Means Clustering

10.5 Radial Basis Functions

10.6 Autoencoders

10.7 Summary

10.8 Further Reading

10.9 Exercises

11 Reinforcement Learning

11.1 Associative Reward-Penalty Algorithm

11.2 Temporal Difference Learning

11.3 Q-Learning

11.4 Summary

11.5 Further Reading

11.6 Exercises

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