Machine Learning in Geomechanics 1

Table of Contents

1. Cover
2. Table of Contents
3. Title Page
4. Copyright Page
5. Preface
6. 1 Overview of Machine Learning in Geomechanics
   1. 1.1. What exactly is machine learning?
   2. 1.2. Classification of ML methods
   3. 1.3. ML and geomechanics
   4. 1.4. Libraries for ML
   5. 1.5. Bias in ML and limitations
   6. 1.6. What to expect from these volumes?
   7. 1.7. Acknowledgments
   8. 1.8. References
7. 2 Introduction to Regression Methods
   1. 2.1. Introduction
   2. 2.2. Linear regression
   3. 2.3. Gradient descent
   4. 2.4. Data preprocessing and model validation
   5. 2.5. Nonlinear regression
   6. 2.6. Regularization techniques
   7. 2.7. Challenges in generalization and extrapolation
   8. 2.8. Bayesian regression
   9. 2.9. Conclusions
   10. 2.10. References
8. 3 Unsupervised Learning: Basic Concepts and Application to Particle Dynamics
   1. 3.1. Introduction
   2. 3.2. Basic concepts
   3. 3.3. Unsupervised learning techniques
   4. 3.4. Application to particle dynamics
   5. 3.5. Conclusion
   6. 3.6. Acknowledgements
   7. 3.7. References
9. 4 Classification Techniques in Machine Learning
   1. 4.1. Introduction
   2. 4.2. Classification techniques
   3. 4.3. AL in classification
   4. 4.4. Conclusion
   5. 4.5. Acknowledgments
   6. 4.6. References
10. 5 Artificial Neural Networks: Learning the Optimum Statistical Model from Data
    1. 5.1. Why PyTorch?
    2. 5.2. Introduction to sampling theory
    3. 5.3. Optimizing a neural network
    4. 5.4. References
11. List of Authors
12. Index
13. Summary of Volume 2
14. End User License Agreement

List of Tables

1. Chapter 1
   1. Table 1.1. Classification of some important methods in ML. The asterisk deno...

List of Illustrations

1. Chapter 1
   1. Figure 1.1. Eighteen times ...