This book inspires me to learn more about deep learning. Especially now I can use a language I am most familiar with to do experiments.
Part 1: Motivation and Basic Concepts
1.1 Artificial Intelligence, Machine Learning, Neural Networks and Deep Learning
1.1.1 Artificial Intelligence
1.1.2 Machine Learning: How It Differs from Traditional Programming
1.1.3 Neural Networks and Deep Learning
1.1.4 Why Deep Learning? Why Now?
1.2.1. Why TensorFlow.js?
1.2.2 What This Book Will and Will Not Teach You To Do with TensorFlow.js
Part 2: A Gentle Introduction to TensorFlow.js
2 Getting Started: Simple Linear Regression in TensorFlow.js
2.1 Example 1: Predicting the duration of a download using TensorFlow.js
2.1.1 A note on code listings and console interactions
2.1.2 Creating and formatting the data
2.1.3 Defining a simple model
2.1.4 Fitting the model to the training data
2.1.5 Using our trained model to make predictions
2.1.6 Summary of our first example
2.2 Inside Model.fit(): Dissecting gradient descent from Example 1
2.2.1 The intuitions behind gradient descent optimization
2.2.2 Backpropagation: Inside gradient descent
2.3 Linear regression with multiple input features
2.3.1 The Boston Housing Prices dataset
2.3.2 Getting and running the Boston-housing project from GitHub
2.3.3 Accessing the Boston-housing data
2.3.4 Precisely defining the Boston-housing problem
2.3.5 A slight diversion into data normalization
2.3.6 Linear regression on the Boston-housing data
2.4 How to interpret your model
2.4.1 Extracting meaning from learned weights
2.4.2 Extracting internal weights from the model
2.4.3 Caveats on interpretability
3 Adding Nonlinearity: Beyond Weighted Sums
4 Recognizing Images and Sounds Using Convolutional Neural Networks
5 Transfer learning: Using pre-trained models for custom tasks
Part 3: Advanced Deep Learning with TensorFlow.js
6 Preparing data for TensorFlow.js
7 Model visualization and tuning
8 Deep learning for text and sequences
9 Generative deep learning in the browser
10 Reinforcement learning in the browser
Part 4: Summary and Closing Words
11 Quick review
12 Final Words
- Deploying computer vision, audio, and natural language processing in the browser
- Fine-tuning machine learning models with client-side data
- Constructing and training a neural network
- Interactive AI for browser games using deep reinforcement learning.
- Generative neural networks to generate music and pictures
- Using TensorFlow.js with Cloud ML
About the authors
Shanqing Cai and Eric Nielsen are senior software engineers on the Google Brain team. Stan Bileschi is the technical lead for Google’s TensorFlow Usability team, which built the TensorFlow Layers API. All three have advanced degrees from MIT. Together, they’re responsible for writing most of TensorFlow.js. François Chollet is a deep-learning researcher at Google and the author of the Keras library.
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This book is terrific for any engineer.
Covers this complex topic in a surprisingly approachable and readable style!