Book Image

Hands-On Artificial Intelligence for IoT - Second Edition

By : Amita Kapoor
Book Image

Hands-On Artificial Intelligence for IoT - Second Edition

By: Amita Kapoor

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 (20 chapters)
Title Page
Copyright and Credits
Dedication
About Packt
Contributors
Preface
Index

Improving your model – tips and tricks


In this chapter, we've learned a large number of ML algorithms, each with its own pros and cons. In this section, we'll look into some common problems and ways to resolve them.

Feature scaling to resolve uneven data scale

The data that's collected normally doesn't have the same scale; for example, one feature may be varying in the range 10–100 and another one may be only distributed in range 2–5. This uneven data scale can have an adverse effect on learning. To resolve this, we use the method of feature scaling (normalization). The choice of normalization has been found to drastically affect the performance of certain algorithms. Two common normalization methods (also called standardization in some books) are as follows:

  • Z-score normalization: In z-score normalization, each individual feature is scaled so that it has the properties of a standard normal distribution, that is, a mean of 0 and variance of 1. If μ is the mean and σ the variance, we can compute...