Practical Tensorflow.Js: Deep Learning in Web App Development (Paperback)

Chapter 1

Welcome to TensorFlow.js

Headings

• ●  What is TensorFlow.js?

• ●  TensorFlow.js API

  ○ Tensors
  ○ Operations ○ Variables

● How to install it

● Use cases

Chapter 2

Building your First Model

Headings

• ●  Building a logistic regression classification model

• ●  Building a linear regression model

• ●  Doing unsupervised learning with k-means

• ●  Dimensionality reduction and visualization with t-SNE and d3.js

• ●  Our first neural network

  Chapter 3

  Create a drawing app to predict handwritten digits using

  Convolutional Neural Networks and MNIST

  Headings
  

• ●  Convolutional Neural Networks

• ●  The MNIST Dataset

• ●  Design the model architecture

• ●  Train the model

• ●  Evaluate the model

• ●  Build the drawing app

• ●  Integrate the model within the app

Chapter 4

"Move your body!" A game featuring PoseNet, a pose estimator model

Headings

• ●  What is PoseNet?

• ●  Loading the model

• ●  Interpreting the result

• ●  Building a game around it

  Chapter 5

  Detect yourself in real-time using an object detection model trained in

  Google Cloud's AutoML

  Headings
  

• ●  TensorFlow Object Detection API

• ●  Google Cloud's AutoML

• ●  Training the model

• ●  Exporting the model and importing it in TensorFlow.js

• ●  Building the webcam app

  Chapter 6

  Transfer Learning with Image Classifier and Voice Recognition

  Headings
  

• ●  What's Transfer Learning?

• ●  MobileNet and ImageNet (MobileNet is the base model and ImageNet is the training set)

• ●  Transferring the knowledge

• ●  Re-training the model

• ●  Testing the model with a video

  Chapter 7

  Censor food you do not like with pix2pix, Generative Adversarial

  Networks, and ml5.js

  Headings
  

• ●  Introduction to Generative Adversarial Networks

• ●  What is image translation?

• ●  Training your custom image translator with pix2pix

• ●  Deploying the model with ml5.js

  Chapter 8

  Detect toxic words from a Chrome Extension using a Universal

  Sentence Encoder

  Headings
  

• ●  Toxicity classifier

• ●  Training the model

• ●  Testing the model

• ●  Integrating the model in a Chrome Extension

  Chapter 9

  Time Series Analysis and Text Generation with Recurrent Neural

  Networks

  Headings
  

• ●  Recurrent Neural Networks

• ●  Example 1: Building an RNN for time series analysis

• ●  Example 2: Building an RNN to generate text

  Chapter 10

  Best practices, integrations with other platforms, remarks and final

  words

  Headings
  

• ●  Best practices

• ●  Integration with other platforms

• ●  Materials for further practice

• ●  Conclusion