Deep Learning with JavaScript /

In Deep Learning with JavaScript, you'll learn to use TensorFlow.js to build deep learning models that run directly in the browser. This fast-paced book, written by Google engineers, is practical, engaging, and easy to follow. Through diverse examples featuring text analysis, speech processing,...

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Detalles Bibliográficos
Autores principales: Bileschi, Stanley (Autor), Cai, Shanqing (Autor), Nielsen, Eric (Autor)
Autor Corporativo: Safari, an O'Reilly Media Company
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Manning Publications, 2020.
Edición:1st edition.
Temas:
TensorFlow.
JavaScript (Computer program language)
Artificial intelligence.
Machine learning.
Artificial Intelligence
JavaScript (Langage de programmation)
Intelligence artificielle.
Apprentissage automatique.
artificial intelligence.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Intro
  • Copyright
  • Brief Table of Contents
  • Table of Contents
  • Foreword
  • Preface
  • Acknowledgments
  • About this Book
  • About the Authors
  • About the cover illustration
  • Part 1. Motivation and basic concepts
  • Chapter 1. Deep learning and JavaScript
  • 1.1. Artificial intelligence, machine learning, neural networks, and deep learning
  • 1.2. Why combine JavaScript and machine learning?
  • 1.3. Why TensorFlow.js?
  • Exercises
  • Summary
  • Part 2. A gentle introduction to TensorFlow.js
  • Chapter 2. Getting started: Simple linear regression in TensorFlow.js
  • 2.1. Example 1: Predicting the duration of a download using TensorFlow.js
  • 2.2. Inside Model.fit(): Dissecting gradient descent from example 1
  • 2.3. Linear regression with multiple input features
  • 2.4. How to interpret your model
  • Exercises
  • Summary
  • Chapter 3. Adding nonlinearity: Beyond weighted sums
  • 3.1. Nonlinearity: What it is and what it is good for
  • 3.2. Nonlinearity at output: Models for classification
  • 3.3. Multiclass classification
  • Exercises
  • Summary
  • Chapter 4. Recognizing images and sounds using convnets
  • 4.1. From vectors to tensors: Representing images
  • 4.2. Your first convnet
  • 4.3. Beyond browsers: Training models faster using Node.js
  • 4.4. Spoken-word recognition: Applying convnets on audio data
  • Exercises
  • Summary
  • Chapter 5. Transfer learning: Reusing pretrained neural networks
  • 5.1. Introduction to transfer learning: Reusing pretrained models
  • 5.2. Object detection through transfer learning on a convnet
  • Exercises
  • Summary
  • Part 3. Advanced deep learning with TensorFlow.js
  • Chapter 6. Working with data
  • 6.1. Using tf.data to manage data
  • 6.2. Training models with model.fitDataset
  • 6.3. Common patterns for accessing data
  • 6.4. Your data is likely flawed: Dealing with problems in your data.
  • 6.5. Data augmentation
  • Exercises
  • Summary
  • Chapter 7. Visualizing data and models
  • 7.1. Data visualization
  • 7.2. Visualizing models after training
  • Materials for further reading and exploration
  • Exercises
  • Summary
  • Chapter 8. Underfitting, overfitting, and the universal workflow of machine learning
  • 8.1. Formulation of the temperature-prediction problem
  • 8.2. Underfitting, overfitting, and countermeasures
  • 8.3. The universal workflow of machine learning
  • Exercises
  • Summary
  • Chapter 9. Deep learning for sequences and text
  • 9.1. Second attempt at weather prediction: Introducing RNNs
  • 9.2. Building deep-learning models for text
  • 9.3. Sequence-to-sequence tasks with attention mechanism
  • Materials for further reading
  • Exercises
  • Summary
  • Chapter 10. Generative deep learning
  • 10.1. Generating text with LSTM
  • 10.2. Variational autoencoders: Finding an efficient and structured vec- ctor representation of images
  • 10.3. Image generation with GANs
  • Materials for further reading
  • Exercises
  • Summary
  • Chapter 11. Basics of deep reinforcement learning
  • 11.1. The formulation of reinforcement-learning problems
  • 11.2. Policy networks and policy gradients: The cart-pole example
  • 11.3. Value networks and Q-learning: The snake game example
  • Materials for further reading
  • Exercises
  • Summary
  • Part 4. Summary and closing words
  • Chapter 12. Testing, optimizing, and deploying models
  • 12.1. Testing TensorFlow.js models
  • 12.2. Model optimization
  • 12.3. Deploying TensorFlow.js models on various platforms and environments
  • Materials for further reading
  • Exercises
  • Summary
  • Chapter 13. Summary, conclusions, and beyond
  • 13.1. Key concepts in review
  • 13.2. Quick overview of the deep-learning workflow and algorithms in TensorFlow.js
  • 13.3. Trends in deep learning.
  • 13.4. Pointers for further exploration
  • Final words
  • Appendix A. Installing tfjs-node-gpu and its dependencies
  • A.1. Installing tfjs-node-gpu on Linux
  • A.2. Installing tfjs-node-gpu on Windows
  • Appendix B.A quick tutorial of tensors and operations in TensorFlow.js
  • B.1. Tensor creation and tensor axis conventions
  • B.2. Basic tensor operations
  • B.3. Memory management in TensorFlow.js: tf.dispose() and tf.tidy()
  • B.4. Calculating gradients
  • Exercises
  • Glossary
  • Index
  • List of Figures
  • List of Tables
  • List of Listings.

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