In the last chapter, we learned about different machine learning (ML) algorithms. The focus of this chapter is neural networks based on multiple layered models, also known as deep learning models. They have become a buzzword in the last few years and an absolute favorite of investors in the field of artificial-intelligence-based startups. Achieving above human level accuracy in the task of object detection and defeating the world's Dan Nine Go master are some of the feats possible by deep learning (DL). In this chapter and a few subsequent chapters, we will learn about the different DL models and how to use DL on our IoT generated data. In this chapter, we will start with a glimpse into the journey of DL, and learn about four popular models, the multilayered perceptron (MLP), the convolutional neural network (CNN), recurrent neural network (RNN), and...
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Hands-On Artificial Intelligence for IoT
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Hands-On Artificial Intelligence for IoT
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Overview of this book
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.
Table of Contents (14 chapters)
Preface
Free Chapter
Principles and Foundations of IoT and AI
Data Access and Distributed Processing for IoT
Machine Learning for IoT
Deep Learning for IoT
Genetic Algorithms for IoT
Reinforcement Learning for IoT
Generative Models for IoT
Distributed AI for IoT
Personal and Home IoT
AI for the Industrial IoT
AI for Smart Cities IoT
Combining It All Together
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