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Hands-On Artificial Intelligence for IoT

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  • 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

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.

  • 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
Page Count 390
Course Length 11 hours 42 minutes
Date Of Publication 31 Jan 2019
What is IoT 101?
Big data and IoT
Infusion of AI – data science in IoT
Tools used in this book
Introduction to genetic algorithms
Coding genetic algorithms using Distributed Evolutionary Algorithms in Python
Why do we need smart cities?
Components of a smart city
Adapting IoT for smart cities and the necessary steps
Challenges and benefits


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.