Author: Juan Rivera Full Title: Practical TensorFlow.js: Deep Learning In Web App Development Publisher: Apress; 1st ed. Edition (October 3, 2020) Year: 2020 ISBN-13: 9781484262733 (978-1-4842-6273-3), 9781484262726 (978-1-4842-6272-6) ISBN-10: 1484262735, 1484262727 Pages: 303 Language: English Genre: Educational: Artificial Intelligence File type: EPUB (True), PDF (True) Quality: 10/10 Price: 37.44 € 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. 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 Features: ✓ Focus less on deep learning concepts, and the functionalities of the framework, and more on actual applications using JavaScript ✓ Work with a wide range of algorithms, methods, and use cases, such as convolutional neural networks, object detection, image translation, and linear regression ✓ Build a real and deployed deep learning product using JavaScript and TensorFlow.js Who This Book Is For: 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. ------------- Our members see more. Join us!