Practical Tensorflow.Js by Juan de Dios Santos Rivera- Bookswagon UAE
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Practical Tensorflow.Js

Practical Tensorflow.Js


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International Edition


About the Book

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


About the Author: Juan De Dios Santos Rivera is a machine learning engineer who focuses on building data-driven and machine learning-driven platforms. As a Big Data Software Engineer for mobile apps, his role has been to build solutions to detect spammers and avoid the proliferation of them. This book goes hand-to-hand with that role in building data solutions. As the AI field keeps growing, developers need to keep extending the reach of our products to every platform out there, which includes web browsers.


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Product Details
  • ISBN-13: 9781484262726
  • Publisher: Apress
  • Publisher Imprint: Apress
  • Height: 234 mm
  • No of Pages: 303
  • Spine Width: 18 mm
  • Weight: 508 gr
  • ISBN-10: 1484262727
  • Publisher Date: 19 Sep 2020
  • Binding: Paperback
  • Language: English
  • Returnable: Y
  • Sub Title: Deep Learning in Web App Development
  • Width: 156 mm


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