<span>Develop and deploy deep learning web apps using the TensorFlow.js library. TensorFlow.βjsβ is part of a bigger framework named TensorFlow, which has many tools that supplement it, such as TensorBoardβ, βml5jsβ, βtfjs-vis. This book will cover all these technologies and show they integrate with
Practical TensorFlow.js: Deep Learning in Web App Development
β Scribed by Juan De Dios Santos Rivera
- Publisher
- Apress
- Year
- 2020
- Tongue
- English
- Leaves
- 318
- Edition
- 1st ed.
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
Develop and deploy deep learning web apps using the TensorFlow.js library. TensorFlow.βjsβ is part of a bigger framework named TensorFlow, which has many tools that supplement it, such as TensorBoardβ, βml5jsβ, βtfjs-vis. This book will cover all these technologies and show they integrate with TensorFlow.βjsβ 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.
What You'll Learn
- 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
Programmers developing deep learning solutions for the web and those who want to learn TensorFlow.js with at least minimal programming and software development knowledge. No prior JavaScript knowledge is required, but familiarity with it is helpful.
β¦ Table of Contents
Front Matter ....Pages i-xxiv
Welcome to TensorFlow.js (Juan De Dios Santos Rivera)....Pages 1-18
Training our first models (Juan De Dios Santos Rivera)....Pages 19-76
Doing k-means with ml5.js (Juan De Dios Santos Rivera)....Pages 77-90
Recognizing handwritten digits with convolutional neural networks (Juan De Dios Santos Rivera)....Pages 91-123
Making a game with PoseNet, a pose estimator model (Juan De Dios Santos Rivera)....Pages 125-149
Identifying toxic text from a Google Chrome Extension (Juan De Dios Santos Rivera)....Pages 151-162
Object detection with a model trained in Google Cloud AutoML (Juan De Dios Santos Rivera)....Pages 163-184
Training an image classifier with transfer learning on Node.js (Juan De Dios Santos Rivera)....Pages 185-201
Time series forecasting and text generation with recurrent neural networks (Juan De Dios Santos Rivera)....Pages 203-239
Generating handwritten digits with generative adversarial networks (Juan De Dios Santos Rivera)....Pages 241-270
Things to remember, whatβs next for you, and final words (Juan De Dios Santos Rivera)....Pages 271-282
Back Matter ....Pages 283-303
β¦ Subjects
Computer Science
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