Practical TensorFlow.js : deep learning in web app development / Juan De Dios Santos Rivera.

Format:

Book

Author/Creator:

Rivera, Juan De Dios Santos, author.

Language:

English

Subjects (All):

Machine learning.

Artificial intelligence.

TensorFlow.

Physical Description:

1 online resource (XXIV, 303 p. 67 illus.)

Edition:

1st ed. 2020.

Place of Publication:

[Place of publication not identified] : Apress, [2020]

System Details:

text file

Summary:

Develop and deploy deep learning web apps using the TensorFlow.js library. TensorFlow. js is part of a bigger framework named TensorFlow, which has many tools that supplement it, such as TensorBoard , ml5js , tfjs-vis. This book will cover all these technologies and show they integrate with TensorFlow. js to create intelligent web apps. The most common and accessible platform users interact with everyday is their web browser, making it an ideal environment to deploy AI systems. TensorFlow.js is a well-known and battle-tested library for creating browser solutions. Working in JavaScript, the so-called language of the web, directly on a browser, you can develop and serve deep learning applications.You'll work with deep learning algorithms such as feedforward neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial network (GAN). Through hands-on examples, apply these networks in use cases related to image classification, natural language processing, object detection, dimensionality reduction, image translation, transfer learning, and time series analysis. Also, these topics are very varied in terms of the kind of data they use, their output, and the training phase. Not everything in machine learning is deep networks, there is also what some call shallow or traditional machine learning. While TensorFlow.js is not the most common place to implement these, you'll be introduce them and review the basics of machine learning through TensorFlow.js. You will: Build deep learning products suitable for web browsers Work with deep learning algorithms such as feedforward neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial network (GAN) Develop apps using image classification, natural language processing, object detection, dimensionality reduction, image translation, transfer learning, and time series analysis.

Contents:

Chapter 1: Welcome to TensorFlow.js

Chapter 2: Training Our First Models

Chapter 3: Doing k-means with ml5.js

Chapter 4: Recognizing Handwritten Digits with Convolutional Neural Networks

Chapter 5: Making a Game with PoseNet, a Pose Estimator Model

Chapter 6: Identifying Toxic Text from a Google Chrome Extension

Chapter 7: Object Detection with a Model Trained in Google Cloud AutoML

Chapter 8: Training an Image Classifier with Transfer Learning on Node.js

Chapter 9: Time Series Forecasting and Text Generation with Recurrent Neural Networks

Chapter 10: Generating Handwritten Digits with Generative Adversarial Networks

Chapter 11: Things to Remember, What's Next for You, and Final Words

Appendix A: Apache License 2.0.

Notes:

Description based on print version record.

ISBN:

1-4842-6273-5