Hands-On Machine Learning with TensorFlow.js
暫譯: 使用 TensorFlow.js 的實作機器學習

Sasaki, Kai

Hands-On Machine Learning with TensorFlow.js
  • 出版商: Packt Publishing
  • 出版日期: 2019-11-27
  • 定價: $1,498
  • 售價: 8.0 折 $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: DeepLearning、TensorFlow、Machine 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

- 在瀏覽器中使用 TensorFlow.js 建立、訓練和運行機器學習模型
- 從零開始創建智能網頁應用程式，並提供有用的範例
- 使用 TensorFlow.js 的靈活且直觀的 API 來理解機器學習演算法的運作

#### 書籍描述

TensorFlow.js 是一個框架，使您能夠創建在網頁瀏覽器中流暢運行的高效能機器學習 (ML) 應用程式。透過本書，您將學習如何使用 TensorFlow.js 透過範例導向的方法實現各種 ML 模型。

從基礎開始，您將了解如何在網頁上構建 ML 模型。接下來，您將熟悉 TensorFlow.js 生態系統，以更有效地開發應用程式。本書將指導您實現 ML 技術和演算法，例如回歸、聚類、快速傅立葉變換 (FFT) 和降維。之後，您將學習 Bellman 方程以解決馬可夫決策過程 (MDP) 問題，並了解其與強化學習的關係。最後，您將探索部署基於 ML 的網頁應用程式和使用 TensorFlow Core 訓練模型的技術。在這本 ML 書中，您將發現有用的提示和技巧，進一步增強您的知識。

在本書結束時，您將具備創建自己的基於網頁的 ML 應用程式和微調模型以達到高效能所需的技能。

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#### 您將學到的內容

- 在 TensorFlow.js 中使用 t-SNE 演算法來降低輸入數據集的維度
- 部署 tfjs-converter 以轉換 Keras 模型並將其加載到 TensorFlow.js 中
- 應用 Bellman 方程來解決 MDP 問題
- 在 TensorFlow.js 中使用 k-means 演算法來可視化預測結果
- 使用 Parcel、Webpack 和 Rollup 創建 tf.js 套件以部署網頁應用程式
- 實現 tf.js 後端框架以調整和加速應用程式性能

#### 本書適合誰

本書適合希望從零開始學習如何將機器學習技術與基於網頁的應用程式整合的網頁開發人員。本書也將吸引數據科學家、機器學習實踐者和深度學習愛好者，他們希望在網頁上使用 TensorFlow.js 執行加速的瀏覽器基礎機器學習。只需具備 JavaScript 程式語言的基本知識即可開始。

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)

佐佐木凱在 Treasure Data 擔任軟體工程師。他專注於開發大規模分散式系統，以使數據變得有價值。他對於透過處理大規模數據來創造人工智慧的熱情使他進入了機器學習領域。他是 TensorFlow.js 的初始貢獻者之一，並持續致力於添加新運算子，以滿足新類型機器學習模型的需求。因為他的工作，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)

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

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