Deep learning : a visual approach /

"A practical, thorough introduction to deep learning, without the usage of advanced math or programming. Covers topics such as image classification, text generation, and the machine learning techniques that are the basis of modern AI"--

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Glassner, Andrew S. (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: San Francisco, CA : No Starch Press, Inc., [2021]
Temas:
Deep learning (Machine learning)
Machine learning.
Neural networks (Computer science)
Apprentissage automatique.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Introduction
  • Part I: Foundational ideas
  • Chapter 1: An overview of machine learning
  • Chapter 2: Essential statistics
  • Chapter 3: Measuring performance
  • Chapter 4: Bayes' Rule
  • Chapter 5: Curves and surfaces
  • Chapter 6: Information theory
  • Part II: Basic machine learning
  • Chapter 8: Training and testing
  • Chapter 9: Overfitting and underfitting
  • Chapter 10: Data preparation
  • Chapter 11: Classifiers
  • Chapter 12: Ensembles
  • Part III: Deep learning basics. Chapter 13: Neural networks
  • Chapter 14: Backpropagation
  • Chpater 15: Optimizers
  • Part IV: Beyond the basics. Chapter 16: Convolutional neural networks
  • Chapter 17: Convnets in practice
  • Chapter 18: Autoencoders
  • Chapter 19: Recurrent neural networks
  • Chapter 20: Attention and transformers
  • Chapter 21: Reinforcement learning
  • Chapter 22: Generatie adversarial networks
  • Chapter 23: Creative applications

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