Deep Learning with JavaScript 1st edition by Stanley Bileschi, Eric Nielsen, Shanqing Cai – Ebook PDF Instant Download/Delivery: 1638351546, 9781638351542
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ISBN 10: 1638351546
ISBN 13: 9781638351542
Author: Stanley Bileschi, Eric Nielsen, Shanqing Cai
Summary Deep learning has transformed the fields of computer vision, image processing, and natural language applications. Thanks to TensorFlow.js, now JavaScript developers can build deep learning apps without relying on Python or R. Deep Learning with JavaScript shows developers how they can bring DL technology to the web. Written by the main authors of the TensorFlow library, this new book provides fascinating use cases and in-depth instruction for deep learning apps in JavaScript in your browser or on Node. Foreword by Nikhil Thorat and Daniel Smilkov. About the technology Running deep learning applications in the browser or on Node-based backends opens up exciting possibilities for smart web applications. With the TensorFlow.js library, you build and train deep learning models with JavaScript. Offering uncompromising production-quality scalability, modularity, and responsiveness, TensorFlow.js really shines for its portability. Its models run anywhere JavaScript runs, pushing ML farther up the application stack. About the book 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, image recognition, and self-learning game AI, you’ll master all the basics of deep learning and explore advanced concepts, like retraining existing models for transfer learning and image generation. What’s inside – Image and language processing in the browser – Tuning ML models with client-side data – Text and image creation with generative deep learning – Source code samples to test and modify About the reader For JavaScript programmers interested in deep learning. About the author Shanging Cai, Stanley Bileschi and Eric D. Nielsen are software engineers with experience on the Google Brain team, and were crucial to the development of the high-level API of TensorFlow.js. This book is based in part on the classic, Deep Learning with Python by François Chollet. TOC: PART 1 – MOTIVATION AND BASIC CONCEPTS 1 • Deep learning and JavaScript PART 2 – A GENTLE INTRODUCTION TO TENSORFLOW.JS 2 • Getting started: Simple linear regression in TensorFlow.js 3 • Adding nonlinearity: Beyond weighted sums 4 • Recognizing images and sounds using convnets 5 • Transfer learning: Reusing pretrained neural networks PART 3 – ADVANCED DEEP LEARNING WITH TENSORFLOW.JS 6 • Working with data 7 • Visualizing data and models 8 • Underfitting, overfitting, and the universal workflow of machine learning 9 • Deep learning for sequences and text 10 • Generative deep learning 11 • Basics of deep reinforcement learning PART 4 – SUMMARY AND CLOSING WORDS 12 • Testing, optimizing, and deploying models 13 • Summary, conclusions, and beyond
Deep Learning with JavaScript 1st Table of contents:
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
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Tags: Stanley Bileschi, Eric Nielsen, Shanqing Cai, Deep Learning, JavaScript