Hands-On Machine Learning with TensorFlow.js

Sasaki, Kai

  • 出版商: Packt Publishing
  • 出版日期: 2019-11-27
  • 定價: $1,498
  • 售價: 8.0$1,198
  • 語言: 英文
  • 頁數: 296
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1838821732
  • ISBN-13: 9781838821739
  • 相關分類: DeepLearningTensorFlowMachine Learning
  • 立即出貨 (庫存=1)

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商品描述

Key Features

  • Build, train and run machine learning models in the browser using TensorFlow.js
  • Create smart web applications from scratch with the help of useful examples
  • Use flexible and intuitive APIs from TensorFlow.js to understand how machine learning algorithms function

Book Description

TensorFlow.js is a framework that enables you to create performant machine learning (ML) applications that run smoothly in a web browser. With this book, you will learn how to use TensorFlow.js to implement various ML models through an example-based approach.

Starting with the basics, you'll understand how ML models can be built on the web. Moving on, you will get to grips with the TensorFlow.js ecosystem to develop applications more efficiently. The book will then guide you through implementing ML techniques and algorithms such as regression, clustering, fast Fourier transform (FFT), and dimensionality reduction. You will later cover the Bellman equation to solve Markov decision process (MDP) problems and understand how it is related to reinforcement learning. Finally, you will explore techniques for deploying ML-based web applications and training models with TensorFlow Core. Throughout this ML book, you'll discover useful tips and tricks that will build on your knowledge.

By the end of this book, you will be equipped with the skills you need to create your own web-based ML applications and fine-tune models to achieve high performance.


What you will learn

  • Use the t-SNE algorithm in TensorFlow.js to reduce dimensions in an input dataset
  • Deploy tfjs-converter to convert Keras models and load them into TensorFlow.js
  • Apply the Bellman equation to solve MDP problems
  • Use the k-means algorithm in TensorFlow.js to visualize prediction results
  • Create tf.js packages with Parcel, Webpack, and Rollup to deploy web apps
  • Implement tf.js backend frameworks to tune and accelerate app performance

Who this book is for

This book is for web developers who want to learn how to integrate machine learning techniques with web-based applications from scratch. This book will also appeal to data scientists, machine learning practitioners, and deep learning enthusiasts who are looking to perform accelerated, browser-based machine learning on Web using TensorFlow.js. Working knowledge of JavaScript programming language is all you need to get started.

商品描述(中文翻譯)

主要特點


  • 使用TensorFlow.js在瀏覽器中建立、訓練和執行機器學習模型

  • 通過實用的示例從頭開始創建智能網絡應用程序

  • 使用TensorFlow.js的靈活且直觀的API,了解機器學習算法的運作方式

書籍描述

TensorFlow.js是一個框架,可以在網頁瀏覽器中創建高效的機器學習(ML)應用程序。通過這本書,您將學習如何使用TensorFlow.js通過基於示例的方法實現各種ML模型。

從基礎知識開始,您將了解如何在網頁上構建ML模型。接著,您將熟悉TensorFlow.js生態系統,以更高效地開發應用程序。本書還將指導您實施回歸、聚類、快速傅立葉變換(FFT)和降維等ML技術和算法。您還將學習使用Bellman方程來解決馬爾可夫決策過程(MDP)問題,並了解它與強化學習的關係。最後,您將探索部署基於ML的網絡應用程序和使用TensorFlow Core訓練模型的技術。在整本書中,您將發現實用的技巧和訣竅,這些將豐富您的知識。

通過閱讀本書,您將具備創建自己的基於網絡的ML應用程序並調整模型以實現高性能所需的技能。



您將學到什麼


  • 使用TensorFlow.js中的t-SNE算法來降低輸入數據集的維度

  • 使用tfjs-converter將Keras模型轉換並加載到TensorFlow.js中

  • 應用Bellman方程來解決MDP問題

  • 使用TensorFlow.js中的k-means算法來可視化預測結果

  • 使用Parcel、Webpack和Rollup創建tf.js包以部署網絡應用程序

  • 實施tf.js後端框架以調整和加速應用程序性能

適合閱讀對象

本書適合希望從頭開始將機器學習技術與基於網絡的應用程序集成的網絡開發人員。本書還適合數據科學家、機器學習從業者和深度學習愛好者,他們希望在Web上使用TensorFlow.js進行加速的基於瀏覽器的機器學習。只需要具備JavaScript編程語言的工作知識,您就可以開始閱讀本書。

作者簡介

Kai Sasaki works as a software engineer at Treasure Data. He engages in developing largescale distributed systems to make data valuable. His passion for creating artificial intelligence by processing large-scale data led him to the field of machine learning. He is one of the initial contributors to TensorFlow.js and keeps working to add new operators that are required for new types of machine learning models. Because of his work, he received the Google Open Source Peer Bonus in 2018.

作者簡介(中文翻譯)

Kai Sasaki 在 Treasure Data 擔任軟體工程師,致力於開發大規模分散式系統,以使數據具有價值。他對通過處理大規模數據創建人工智能的熱情使他進入了機器學習領域。他是 TensorFlow.js 的最初貢獻者之一,並不斷努力添加新的運算子,以滿足新型機器學習模型的需求。由於他的工作,他在2018年獲得了 Google 開源同行獎金。

目錄大綱

Table of Contents

  1. Machine Learning for the Web
  2. Importing Pre-trained Models into TensorFlow.js
  3. TensorFlow.js Ecosystem
  4. Polynomial Regression
  5. Classification with Logistic Regression
  6. Unsupervised Learning
  7. Sequential Data Analysis
  8. Dimensionality Reduction
  9. Solving Markov decision problems
  10. Deploying Machine Learning Applications
  11. Tuning applications to achieve high performance
  12. Future Works around TensorFlow.js

目錄大綱(中文翻譯)

目錄


  1. 網頁機器學習

  2. 將預訓練模型導入到TensorFlow.js

  3. TensorFlow.js生態系統

  4. 多項式回歸

  5. 使用邏輯回歸進行分類

  6. 無監督學習

  7. 序列數據分析

  8. 降維

  9. 解決馬爾可夫決策問題

  10. 部署機器學習應用

  11. 調整應用以實現高性能

  12. 關於TensorFlow.js的未來工作