Machine Learning for the Physical Sciences

Machine Learning for the Physical Sciences portes grátis

Machine Learning for the Physical Sciences

Fundamentals and Prototyping with Julia

Requiao da Cunha, Carlo

Taylor & Francis Ltd

12/2023

266

Dura

Inglês

9781032392295

15 a 20 dias

Descrição não disponível.
Chapter 1: Multivariate Calculus. Chapter 2: Probability Theory. Chapter 3: Dimensionality Reduction. Chapter 4: Cluster Analysis. Chapter 5: Vector Quantization Techniques. Chapter 6: Regression Models. Chapter 7: Classification. Chapter 8: Feedforward Networks. Chapter 9: Advanced Network Architectures. Chapter 10: Value Methods. Chapter 11: Gradient Methods. Chapter 12: Population-Based Metaheuristic Methods. Chapter 13: Local Search methods. Appendix A: Sufficient Statistic. Appendix B: Graphs. Appendix C: Sequential Minimization Optimization. Appendix D: Algorithmic Differentiation. Appendix E: Batch Normalizing Transform. Appendix F: Divergence of Two Gaussian Distributions. Appendix G: Continuous-time Bellman's Equation. Appendix H: Conjugate Gradient. Appendix I: Importance Sampling. References. Index.
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multivariate calculus;probability theory;dimensionality reduction;cluster analysis;regression models;metaheuristic optimization;advanced machine learning techniques for physics