book

Deep Learning with JavaScript

Name: Deep Learning with JavaScript
ISBN: 9781617296178

by Stanley Bileschi, Eric Nielsen, Shanqing Cai

February 2020

Intermediate to advanced

560 pages

22h 18m

English

Manning Publications

Audiobook available

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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 learning1.2. Why combine JavaScript and machine learning?1.3. Why TensorFlow.js?ExercisesSummary
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.js2.2. Inside Model.fit(): Dissecting gradient descent from example 12.3. Linear regression with multiple input features2.4. How to interpret your modelExercisesSummary
Chapter 3. Adding nonlinearity: Beyond weighted sums
3.1. Nonlinearity: What it is and what it is good for3.2. Nonlinearity at output: Models for classification3.3. Multiclass classificationExercisesSummary
Chapter 4. Recognizing images and sounds using convnets
4.1. From vectors to tensors: Representing images4.2. Your first convnet4.3. Beyond browsers: Training models faster using Node.js4.4. Spoken-word recognition: Applying convnets on audio dataExercisesSummary
Chapter 5. Transfer learning: Reusing pretrained neural networks
5.1. Introduction to transfer learning: Reusing pretrained models5.2. Object detection through transfer learning on a convnetExercisesSummary
Part 3. Advanced deep learning with TensorFlow.js
Chapter 6. Working with data
6.1. Using tf.data to manage data6.2. Training models with model.fitDataset6.3. Common patterns for accessing data6.4. Your data is likely flawed: Dealing with problems in your data6.5. Data augmentationExercisesSummary
Chapter 7. Visualizing data and models
7.1. Data visualization7.2. Visualizing models after trainingMaterials for further reading and explorationExercisesSummary
Chapter 8. Underfitting, overfitting, and the universal workflow of machine learning
8.1. Formulation of the temperature-prediction problem8.2. Underfitting, overfitting, and countermeasures8.3. The universal workflow of machine learningExercisesSummary
Chapter 9. Deep learning for sequences and text
9.1. Second attempt at weather prediction: Introducing RNNs9.2. Building deep-learning models for text9.3. Sequence-to-sequence tasks with attention mechanismMaterials for further readingExercisesSummary
Chapter 10. Generative deep learning
10.1. Generating text with LSTM10.2. Variational autoencoders: Finding an efficient and structured vec- ctor representation of images10.3. Image generation with GANsMaterials for further readingExercisesSummary
Chapter 11. Basics of deep reinforcement learning
11.1. The formulation of reinforcement-learning problems11.2. Policy networks and policy gradients: The cart-pole example11.3. Value networks and Q-learning: The snake game exampleMaterials for further readingExercisesSummary
Part 4. Summary and closing words
Chapter 12. Testing, optimizing, and deploying models
12.1. Testing TensorFlow.js models12.2. Model optimization12.3. Deploying TensorFlow.js models on various platforms and environmentsMaterials for further readingExercisesSummary
Chapter 13. Summary, conclusions, and beyond
13.1. Key concepts in review13.2. Quick overview of the deep-learning workflow and algorithms in TensorFlow.js13.3. Trends in deep learning13.4. Pointers for further explorationFinal 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

Content preview from Deep Learning with JavaScript

B.1. Tensor creation and tensor axis conventions

Remember that a tensor is simply a data container. Every tensor has two fundamental properties: data type (dtype) and shape. dtype controls what kinds of values are stored within the tensor. A given tensor can store only one kind of value. At the time of this writing (version 0.13.5), the supported dtypes are float32, int32, and bool.

The shape is an array of integers indicating how many elements are in the tensor and how they are organized. It can be thought of as the “shape and size” of the container that is the tensor (see figure B.1).

Figure B.1. Examples of tensors of rank 0, 1, 2, 3, and 4

The length of the shape is known as the tensor’s rank. For example, a 1D tensor, also known as ...

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Deep Learning with JavaScript

by Stanley Bileschi, Eric Nielsen, Shanqing Cai

B.1. Tensor creation and tensor axis conventions

Figure B.1. Examples of tensors of rank 0, 1, 2, 3, and 4

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