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Deep Learning Foundations and Concepts /

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This book offers a comprehensive introduction to the central ideas that underpin deep learning. It is intended both for newcomers to machine learning and for those already experienced in the field. Covering key concepts relating to contemporary architectures and techniques, this essential book equip...

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Detalles Bibliográficos
Main Authors: Bishop, Christopher M. (Author), Bishop, Hugh (Author)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:inglés
Publicado: Cham : Springer International Publishing : Imprint: Springer, 2024.
Edición:1st ed. 2024.
Subjects:
Artificial intelligence.
Machine learning.
Artificial intelligence > Data processing.
Artificial Intelligence.
Machine Learning.
Data Science.
Acceso en liña:https://doi.org/10.1007/978-3-031-45468-4
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Table of Contents:
  • Preface
  • The Deep Learning Revolution
  • Probabilities
  • Standard Distributions
  • Single-layer Networks: Regression
  • Single-layer Networks: Classification
  • Deep Neural Networks
  • Gradient Descent
  • Backpropagation
  • Regularization
  • Convolutional Networks
  • Structured Distributions
  • Transformers
  • Graph Neural Networks
  • Sampling
  • Discrete Latent Variables
  • Continuous Latent Variables
  • Generative Adversarial Networks
  • Normalizing Flows
  • Autoencoders
  • Diffusion Models
  • Appendix A Linear Algebra
  • Appendix B Calculus of Variations
  • Appendix C Lagrange Multipliers
  • Biblyography
  • Index.

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