Hands-On Artificial Intelligence for IoT: Expert machine learning and deep learning techniques for developing smarter IoT systems

by Amita Kapoor

Build smarter systems by combining artificial intelligence and the Internet of Things—two of the most talked about topics today

Key FeaturesLeverage the power of Python libraries such as TensorFlow and Keras to work with real-time IoT dataProcess IoT data and predict outcomes in real time to build smart IoT modelsCover practical case studies on industrial IoT, smart cities, and home automationBook DescriptionThere 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 learnApply different AI techniques including machine learning and deep learning using TensorFlow and KerasAccess and process data from various distributed sourcesPerform supervised and unsupervised machine learning for IoT dataImplement distributed processing of IoT data over Apache Spark using the MLLib and H2O.ai platformsForecast time-series data using deep learning methodsImplementing AI from case studies in Personal IoT, Industrial IoT, and Smart CitiesGain unique insights from data obtained from wearable devices and smart devicesWho this book is forIf 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 ContentsPrinciples and Foundations of IoT and AI Data Access and Distributed Processing for IoTMachine Learning for IoTDeep Learning for IoTGenetic Algorithms for IoTReinforcement Learning for IoTGAN for IoTDistributed AI for IoTPersonal and Home and IoTAI for Industrial IoTAI for Smart Cities IoTCombining It All Together

392 pages, Kindle Edition

Published January 31, 2019

About the author

Amita Kapoor is 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 masters in Electronics in 1996 and Ph.D. in 2011, during Ph.D. she was awarded prestigious DAAD fellowship to pursue a part of her research work in 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 ML, AI, Deep Reinforcement Learning and Robotics.

Reviews

[Safayet Khan · Rating 5 out of 5]

Usually when I start reading a technical book, inner part of me has always wished that if it has a nice general introduction, maybe not so much technical but giving me a sense about the topics that it will be covering. Because for a person like me who just have finished his undergrad, two years ago, it’s really hard and time consuming to decode a technical book. So, I always feel safe in which book I am spending my time on!

Now when I started reading this book, I just loved the introduction. The author combines IoT, Big Data and AI in so simplistic writing, but yet the writing was so much charming. One thing I just have to say I did the course on Coursera by Andrew Ng on Deep Learning. He said, “AI is the new electricity.” This beautiful quotation is rephrased by the author as, “If AI is the new electricity, data is the coal, and IoT the new coal-mine.” Whenever I was stuck and tired while reading this book, I remembered this rephrased quotation by the author and for me it was inspiring!

Next comes to the technical side of this book, useful data format processing is shown in details with hands-on coding-based approach. Then the book goes into ML covering linear regression, logistic regression, SVM, Naïve Bayes, Decision trees. After that it’s Deep Learning then genetic algorithms then reinforcement learning then generative models and lots of examples at the end of the book that uses combinations of IoT, Big Data and AI. If you asked me what is the best feature of this book, I would tell you that the further reading materials. They are not listed at the end of the book or end of a chapter, but according to the necessity, they are everywhere in the book and the author gives you hints what to expect from those further reading.

I just loved reading and coding together. This book was excellent and helpful for me.

[Abhisek · Rating 5 out of 5]

The book is very well written. The concepts are very well explained