Practical Tensorflow.Js: Deep Learning in Web App Development
暫譯: 實用 TensorFlow.js:網頁應用程式開發中的深度學習

Rivera, Juan de Dios Santos

  • 出版商: Apress
  • 出版日期: 2020-09-19
  • 售價: $2,100
  • 貴賓價: 9.5$1,995
  • 語言: 英文
  • 頁數: 303
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1484262727
  • ISBN-13: 9781484262726
  • 相關分類: DeepLearningTensorFlow
  • 立即出貨 (庫存=1)

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相關主題

商品描述

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.

商品描述(中文翻譯)

使用 TensorFlow.js 庫開發和部署深度學習網頁應用程式。TensorFlow.js 是一個更大框架 TensorFlow 的一部分,該框架有許多輔助工具,如 TensorBoard、ml5js 和 tfjs-vis。本書將涵蓋所有這些技術,並展示它們如何與 TensorFlow.js 整合以創建智能網頁應用程式。

用戶每天互動的最常見且可接觸的平台是他們的網頁瀏覽器,使其成為部署 AI 系統的理想環境。TensorFlow.js 是一個知名且經過實戰考驗的庫,用於創建瀏覽器解決方案。在瀏覽器中直接使用 JavaScript 這種所謂的網頁語言,您可以開發和提供深度學習應用程式。您將使用深度學習算法,如前饋神經網絡、卷積神經網絡 (CNN)、遞迴神經網絡 (RNN) 和生成對抗網絡 (GAN)。通過實作範例,將這些網絡應用於與圖像分類、自然語言處理、物體檢測、降維、圖像翻譯、遷移學習和時間序列分析相關的案例。

此外,這些主題在所使用的數據類型、輸出和訓練階段方面非常多樣化。機器學習中並非所有內容都是深度網絡,還有一些人稱之為淺層或傳統機器學習的內容。雖然 TensorFlow.js 不是實現這些的最常見場所,但您將會接觸到它們,並通過 TensorFlow.js 回顧機器學習的基本概念。

您將學到的內容


  • 構建適合網頁瀏覽器的深度學習產品


  • 使用深度學習算法,如前饋神經網絡、卷積神經網絡 (CNN)、遞迴神經網絡 (RNN) 和生成對抗網絡 (GAN)


  • 開發使用圖像分類、自然語言處理、物體檢測、降維、圖像翻譯、遷移學習和時間序列分析的應用程式



本書適合對象

本書適合為網頁開發深度學習解決方案的程式設計師,以及希望學習 TensorFlow.js 的人,前提是具備至少基本的程式設計和軟體開發知識。不需要先前的 JavaScript 知識,但熟悉它會有所幫助。

作者簡介

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.

作者簡介(中文翻譯)

胡安·德·迪奧斯·聖托斯·里維拉是一位專注於構建數據驅動和機器學習驅動平台的機器學習工程師。作為移動應用程式的大數據軟體工程師,他的角色是構建解決方案以檢測垃圾郵件發送者並避免其擴散。本書與該角色密切相關,旨在構建數據解決方案。隨著人工智慧領域的不斷增長,開發人員需要不斷擴展我們產品的覆蓋範圍,涵蓋所有平台,包括網頁瀏覽器。