Statistical Learning Using Neural Networks: A Guide for Statisticians and Data Scientists with Python

✍ Scribed by Pereira, Basilio de Braganoca;Rao, C Radhakrishna(Contributor);Oliveira, Fabio Borges De(Contributor)

• Publisher: CRC Press
• Year: 2020
• Tongue: English
• Leaves: 248
• Category: Library

✦ Synopsis

Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Pythonintroduces artificial neural networks starting from the basics and increasingly demanding more effort from readers, who can learn the theory and its applications in statistical methods with concrete Python code examples. It presents a wide range of widely used statistical methodologies, applied in several research areas with Python code examples, which are available online. It is suitable for scientists and developers as well as graduate students.



Key Features:



Discusses applications in several research areas



Covers a wide range of widely used statistical methodologies



Includes Python code examples



Gives numerous neural network models

This book covers fundamental concepts on Neural Networks including Multivariate Statistics Neural Networks, Regression Neural Network Models, Survival Analysis Networks, Time Series Forecasting Networks, Control Chart Networks, and Statistical Inference Results.



This book is suitable for both teaching and research. It introduces neural networks and is a guide for outsiders of academia working in data mining and artificial intelligence (AI). This book brings together data analysis from statistics to computer science using neural networks.

✦ Table of Contents

Cover......Page 1
Half Title......Page 2
Title Page......Page 4
Copyright Page......Page 5
Dedication......Page 6
Table of Contents......Page 8
Preface......Page 12
Acknowledgments......Page 14
1 Introduction......Page 16
2.1 Artificial Intelligence: Symbolist and Connectionist......Page 18
2.2 The Brain and Neural Networks......Page 19
2.3 Artificial Neural Networks and Diagrams......Page 20
2.5 Network Architectures......Page 23
2.6 Network Training......Page 27
2.7 Kolmogorov Theorem......Page 28
2.8 Model Choice......Page 30
2.8.2 Bias-variance Trade-off: Early Stopping Method of Training......Page 31
2.8.3 Choice of Structure......Page 32
2.8.5 Network Pruning......Page 33
2.9 McCulloch-Pitt Neuron......Page 34
2.10 Rosenblatt Perceptron......Page 35
2.11 Widrow’s Adaline and Madaline......Page 37
2.12 Terminology......Page 38
2.13 Running Python in a Nutshell......Page 43
3.1 Multilayer Feedforward Networks......Page 48
3.2 Associative and Hopfield Networks......Page 52
3.3 Radial Basis Function Networks......Page 60
3.4 Wavelet Neural Networks......Page 61
3.4.1 Wavelets......Page 63
3.5 Mixture-of-Experts Networks......Page 66
3.6 Neural Network and Statistical Model Interfaces......Page 70
3.7.1 Fitting Data......Page 71
3.7.2 Classification......Page 73
3.7.3 Hopfield Networks......Page 77
4.1.1 Competitive Networks......Page 82
4.1.2 Learning Vector Quantization (LVQ)......Page 85
4.1.3 Adaptive Resonance Theory (ART) Networks......Page 87
4.1.4 Self-Organizing Maps (SOM) Networks......Page 93
4.2 Dimensional Reduction Networks......Page 102
4.2.1 Basic Structure of Data Matrix......Page 103
4.2.2.1 Principal Components Analysis (PCA)......Page 105
4.2.2.4 Correspondence Analysis (CA)......Page 106
4.2.2.5 Multidimensional Scaling......Page 107
4.2.2.6 Independent Component Analysis (ICA)......Page 108
4.2.3 PCA Networks......Page 112
4.2.5 FA Networks......Page 117
4.2.6 Correspondence Analysis (CA) Networks......Page 118
4.2.7 Independent Component Analysis (ICA) Networks......Page 121
4.3 Classification Networks......Page 123
4.4 Multivariate Statistics Neural Network Models with Python......Page 135
4.4.1 Clustering......Page 136
4.4.2 Fitting Data......Page 141
5.1 Generalized Linear Model Networks (GLIMNs)......Page 146
5.1.1 Logistic Regression Networks......Page 147
5.1.2 Regression Networks......Page 151
5.2.1 Probabilistic Neural Networks (PNNs)......Page 154
5.2.2 General Regression Neural Networks (GRNNs)......Page 155
5.2.3 Generalized Additive Model Networks......Page 156
5.2.4 Regression and Classification Tree Networks......Page 158
5.2.5 Projection Pursuit and Feedforward Networks......Page 160
5.2.6 Example......Page 161
5.3 Regression Neural Network Models with Python......Page 162
6.1 Survival Analysis Networks......Page 166
6.2 Time Series Forecasting......Page 173
6.2.1 Forecasting with Neural Networks......Page 178
6.3 Control Chart Networks......Page 182
6.4 Some Statistical Inference Results......Page 185
6.4.1 Estimation Methods......Page 186
6.4.2 Bayesian Methods......Page 187
6.4.3 Interval Estimation......Page 190
6.4.4 Statistical Tests......Page 191
6.5 Forecasting with Python......Page 192
A Command Reference......Page 198
Bibliography......Page 230
Index......Page 248