Artificial Intelligence for IoT Cookbook: Over 70 recipes for building AI solutions for smart homes, industrial IoT, and smart cities

Roshak, Michael

  • 出版商: Packt Publishing
  • 出版日期: 2021-03-05
  • 售價: $1,660
  • 貴賓價: 9.5$1,577
  • 語言: 英文
  • 頁數: 260
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1838981985
  • ISBN-13: 9781838981983
  • 相關分類: 人工智慧物聯網 IoT
  • 相關翻譯: 人工智能與物聯網 (簡中版)
  • 海外代購書籍(需單獨結帳)


Implement machine learning and deep learning techniques to perform predictive analytics on real-time IoT data

Key Features

  • Discover quick solutions to common problems that you'll face while building smart IoT applications
  • Implement advanced techniques such as computer vision, NLP, and embedded machine learning
  • Build, maintain, and deploy machine learning systems to extract key insights from IoT data

Book Description

Artificial intelligence (AI) is rapidly finding practical applications across a wide variety of industry verticals, and the Internet of Things (IoT) is one of them. Developers are looking for ways to make IoT devices smarter and to make users' lives easier. With this AI cookbook, you'll be able to implement smart analytics using IoT data to gain insights, predict outcomes, and make informed decisions, along with covering advanced AI techniques that facilitate analytics and learning in various IoT applications.

Using a recipe-based approach, the book will take you through essential processes such as data collection, data analysis, modeling, statistics and monitoring, and deployment. You'll use real-life datasets from smart homes, industrial IoT, and smart devices to train and evaluate simple to complex models and make predictions using trained models. Later chapters will take you through the key challenges faced while implementing machine learning, deep learning, and other AI techniques, such as natural language processing (NLP), computer vision, and embedded machine learning for building smart IoT systems. In addition to this, you'll learn how to deploy models and improve their performance with ease.

By the end of this book, you'll be able to package and deploy end-to-end AI apps and apply best practice solutions to common IoT problems.

What you will learn

  • Explore various AI techniques to build smart IoT solutions from scratch
  • Use machine learning and deep learning techniques to build smart voice recognition and facial detection systems
  • Gain insights into IoT data using algorithms and implement them in projects
  • Perform anomaly detection for time series data and other types of IoT data
  • Implement embedded systems learning techniques for machine learning on small devices
  • Apply pre-trained machine learning models to an edge device
  • Deploy machine learning models to web apps and mobile using TensorFlow.js and Java

Who this book is for

If you're an IoT practitioner looking to incorporate AI techniques to build smart IoT solutions without having to trawl through a lot of AI theory, this AI IoT book is for you. Data scientists and AI developers who want to build IoT-focused AI solutions will also find this book useful. Knowledge of the Python programming language and basic IoT concepts is required to grasp the concepts covered in this artificial intelligence book more effectively.


Michael Roshak is a cloud architect and strategist with extensive subject matter expertise in enterprise cloud transformation programs and infrastructure modernization through designing, and deploying cloud-oriented solutions and architectures. He is responsible for providing strategic advisory for cloud adoption, consultative technical sales, and driving broad cloud services consumption with highly strategic accounts across multiple industries. --This text refers to the paperback edition.


Table of Contents

  1. Setting up the IoT and AI Environment
  2. Handling Data
  3. Machine Learning for IoT
  4. Deep Learning for Predictive Maintenance
  5. Anomaly Detection
  6. Computer Vision
  7. NLP and Bots for Self-Ordering Kiosk
  8. Optimizing with Microcontrollers and Pipelines
  9. Deploying to the Edge