Practical Tensorflow.Js: Deep Learning in Web App Development

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.
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
  

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.

使用TensorFlow.js库开发和部署深度学习Web应用程序。TensorFlow.js是一个更大的框架TensorFlow的一部分，它有许多补充工具，如TensorBoard、ml5js和tfjs-vis。本书将涵盖所有这些技术，并展示它们如何与TensorFlow.js集成以创建智能Web应用程序。
用户每天与之互动最常见和可访问的平台是Web浏览器，使其成为部署AI系统的理想环境。TensorFlow.js是一个广为人知且经过实战验证的用于创建浏览器解决方案的库。通过在Web浏览器上直接使用JavaScript进行开发，您可以开发和提供深度学习应用程序。您将使用前馈神经网络、卷积神经网络（CNN）、循环神经网络（RNN）和生成对抗网络（GAN）等深度学习算法。通过实际示例，将这些网络应用于与图像分类、自然语言处理、目标检测、降维、图像翻译、迁移学习和时间序列分析相关的用例中。

此外，这些主题在使用的数据类型、输出和训练阶段方面非常多样化。机器学习并不全是深度网络，还有一些人称之为浅层或传统机器学习的内容。虽然TensorFlow.js不是实现这些内容的最常见地方，但您将介绍它们并通过TensorFlow.js回顾机器学习的基础知识。
您将学到什么

• 构建适用于Web浏览器的深度学习产品
  
  


• 使用前馈神经网络、卷积神经网络（CNN）、循环神经网络（RNN）和生成对抗网络（GAN）等深度学习算法
  
  


• 开发使用图像分类、自然语言处理、目标检测、降维、图像翻译、迁移学习和时间序列分析的应用程序
  
  

本书适合人群

为Web开发深度学习解决方案的程序员以及希望学习TensorFlow.js的人，至少需要具备最基本的编程和软件开发知识。不需要先前的JavaScript知识，但熟悉它会有所帮助。

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.

Juan De Dios Santos Rivera 是一位專注於建立以數據驅動和機器學習驅動的平台的機器學習工程師。作為一名移動應用程式的大數據軟體工程師，他的角色是建立解決方案來檢測垃圾郵件發送者並避免其擴散。這本書與他在建立數據解決方案的角色相輔相成。隨著人工智慧領域的不斷發展，開發人員需要將我們的產品擴展到各種平台，包括網頁瀏覽器。