Hands-On Artificial Intelligence for IoT: Expert machine learning and deep learning techniques for developing smarter IoT systems
Authors: Amita Kapoor
ISBN-10: 1788836065
ISBN-13: 9781788836067
Released: 2019-01-31
Print Length 页数: 390 pages
Publisher finelybook 出版社: Packt
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.
Contents
1: PRINCIPLES AND FOUNDATIONS OF IOT AND AI
2: DATA ACCESS AND DISTRIBUTED PROCESSING FOR IOT
3: MACHINE LEARNING FOR IOT
4: DEEP LEARNING FOR IOT
5: GENETIC ALGORITHMS FOR IOT
6: REINFORCEMENT LEARNING FOR IOT
7: GENERATIVE MODELS FOR IOT
8: DISTRIBUTED AI FOR IOT
9: PERSONAL AND HOME IOT
10: AI FOR THE INDUSTRIAL IOT
11: AI FOR SMART CITIES IOT
12: COMBINING IT ALL TOGETHER
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
Authors
Amita Kapoor
Amita Kapoor is an associate professor in the Department of Electronics,SRCASW,University of Delhi,and has been actively teaching neural networks and artificial intelligence for the last 20 years. She completed her master’s in electronics in 1996 and her PhD in 2011. During her PhD she was awarded the prestigious DAAD fellowship to pursue part of her research at the Karlsruhe Institute of Technology,Karlsruhe,Germany. She was awarded the Best Presentation Award at the Photonics 2008 international conference. She is an active member of ACM,AAAI,IEEE,and INNS. She has co-authored two books. She has more than 40 publications in international journals and conferences. Her present research areas include machine learning,artificial intelligence,deep reinforcement learning,and robotics.
