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

Hands-On Artificial Intelligence for IoT: Expert machine learning and deep learning techniques for developing smarter IoT systems
Hands-On Artificial Intelligence for IoT: Expert machine learning and deep learning techniques for developing smarter IoT systems by Amita Kapoor
English | 2019 | ISBN: 1788836067 | 390 Pages | EPUB | 54 MB

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