Hands-On Artificial Intelligence for IoT: Expert machine learning and deep learning techniques for developing smarter IoT systems
Amita Kapoor
- 出版商: Packt Publishing
- 出版日期: 2019-01-31
- 售價: $1,650
- 貴賓價: 9.5 折 $1,568
- 語言: 英文
- 頁數: 390
- 裝訂: Paperback
- ISBN: 1788836065
- ISBN-13: 9781788836067
-
相關分類:
人工智慧、Machine Learning、DeepLearning、物聯網 IoT
-
相關翻譯:
AIoT 系統開發:基於機器學習和 Python 深度學習 (簡中版)
立即出貨 (庫存=1)
買這商品的人也買了...
-
$158人工智能中的深度結構學習 (Learning Deep Architectures for Ai)
-
$888OpenCV 3.x with Python By Example, 2/e
-
$200人工智能基礎 (高中版)
-
$620$527 -
$1,650$1,617 -
$2,020$1,919 -
$580$458 -
$400$360 -
$393Python 人臉識別:從入門到工程實踐
-
$380$300 -
$7,200$6,840 -
$1,320$1,254 -
$3,680$3,496 -
$780$616 -
$780$663 -
$556電腦視覺與深度學習實戰:以 MATLAB、Python 為工具
-
$1,000$850 -
$1,650$1,568 -
$1,000$850 -
$207算法設計指南, 2/e (The Algorithm Design Manual, 2/e)
-
$454深度實踐 OCR:基於深度學習的文字識別
-
$690$587 -
$460$414 -
$607深度學習之人臉圖像處理:核心算法與案例實戰
-
$600$468
相關主題
商品描述
Build smarter systems by combining artificial intelligence and the Internet of Things―two of the most talked about topics today
Key Features
- Leverage the power of Python libraries such as TensorFlow and Keras to work with real-time IoT data
- Process IoT data and predict outcomes in real time to build smart IoT models
- Cover practical case studies on industrial IoT, smart cities, and home automation
Book Description
There are many applications that use data science and analytics to gain insights from terabytes of data. These apps, however, do not address the challenge of continually discovering patterns for IoT data. In Hands-On Artificial Intelligence for IoT, we cover various aspects of artificial intelligence (AI) and its implementation to make your IoT solutions smarter.
This book starts by covering the process of gathering and preprocessing IoT data gathered from distributed sources. You will learn different AI techniques such as machine learning, deep learning, reinforcement learning, and natural language processing to build smart IoT systems. You will also leverage the power of AI to handle real-time data coming from wearable devices. As you progress through the book, techniques for building models that work with different kinds of data generated and consumed by IoT devices such as time series, images, and audio will be covered. Useful case studies on four major application areas of IoT solutions are a key focal point of this book. In the concluding chapters, you will leverage the power of widely used Python libraries, TensorFlow and Keras, to build different kinds of smart AI models.
By the end of this book, you will be able to build smart AI-powered IoT apps with confidence.
What you will learn
- Apply different AI techniques including machine learning and deep learning using TensorFlow and Keras
- Access and process data from various distributed sources
- Perform supervised and unsupervised machine learning for IoT data
- Implement distributed processing of IoT data over Apache Spark using the MLLib and H2O.ai platforms
- Forecast time-series data using deep learning methods
- Implementing AI from case studies in Personal IoT, Industrial IoT, and Smart Cities
- Gain unique insights from data obtained from wearable devices and smart devices
Who this book is for
If you are a data science professional or a machine learning developer looking to build smart systems for IoT, Hands-On Artificial Intelligence for IoT is for you. If you want to learn how popular artificial intelligence (AI) techniques can be used in the Internet of Things domain, this book will also be of benefit. A basic understanding of machine learning concepts will be required to get the best out of this book.
Table of Contents
- Principles and Foundations of IoT and AI
- Data Access and Distributed Processing for IoT
- Machine Learning for IoT
- Deep Learning for IoT
- Genetic Algorithms for IoT
- Reinforcement Learning for IoT
- GAN for IoT
- Distributed AI for IoT
- Personal and Home and IoT
- AI for Industrial IoT
- AI for Smart Cities IoT
- Combining It All Together
商品描述(中文翻譯)
結合人工智慧和物聯網兩個當今最熱門的話題,打造更智能的系統。
主要特點:
- 利用Python庫,如TensorFlow和Keras,處理即時物聯網數據
- 實時處理物聯網數據並預測結果,建立智能物聯網模型
- 涵蓋工業物聯網、智慧城市和家庭自動化等實際案例
書籍描述:
許多應用程式使用數據科學和分析從大量數據中獲取洞察力。然而,這些應用程式並未解決持續發現物聯網數據模式的挑戰。在《Hands-On Artificial Intelligence for IoT》中,我們涵蓋了人工智慧(AI)的各個方面及其在物聯網解決方案中的實施,使您的物聯網解決方案更智能。
本書首先介紹了從分散源收集和預處理物聯網數據的過程。您將學習不同的AI技術,如機器學習、深度學習、強化學習和自然語言處理,以建立智能物聯網系統。您還將利用AI處理來自可穿戴設備的即時數據。隨著您閱讀本書,將涵蓋使用不同類型的物聯網設備生成和消耗的各種數據(如時間序列、圖像和音頻)建立模型的技術。本書的重點是四個主要應用領域的實用案例。在結尾章節中,您將利用廣泛使用的Python庫TensorFlow和Keras來建立不同類型的智能AI模型。
通過閱讀本書,您將能夠自信地建立智能AI驅動的物聯網應用程式。
學到的內容:
- 使用TensorFlow和Keras等不同的AI技術,包括機器學習和深度學習
- 存取和處理來自不同分散源的數據
- 對物聯網數據進行監督和非監督機器學習
- 使用MLLib和H2O.ai平台在Apache Spark上實現物聯網數據的分散處理
- 使用深度學習方法預測時間序列數據
- 在個人物聯網、工業物聯網和智慧城市物聯網案例中實施人工智慧
- 從可穿戴設備和智能設備獲取的數據獲得獨特的洞察力
本書適合對象:
- 數據科學專業人士或機器學習開發人員,希望為物聯網建立智能系統
- 如果您想了解如何在物聯網領域中應用流行的人工智慧(AI)技術,本書也將對您有所幫助。您需要基本的機器學習概念,以充分利用本書的內容。
目錄:
1. 物聯網和人工智慧的原理和基礎
2. 物聯網的數據存取和分散處理
3. 物聯網的機器學習
4. 物聯網的深度學習
5. 物聯網的遺傳算法
6. 物聯網的強化學習
7. 物聯網的生成對抗網絡
8. 物聯網的分散人工智慧
9. 個人和家庭物聯網
10. 工業物聯網的人工智慧
11. 智慧城市物聯網的人工智慧
12. 綜合應用