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,...
Main Authors: | , , |
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Format: | eBook |
Language: | English |
Published: |
Manning Publications
2020
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Edition: | 1st edition |
Subjects: | |
Online Access: | |
Collection: | O'Reilly - Collection details see MPG.ReNa |
Table of Contents:
- 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
- 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
- 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