This chapter focused on providing the reader with tools to handle different types of data and how to prepare them for the deep learning models. We started with time series data. This chapter next detailed how textual data needs to be preprocessed. This chapter showed how to perform data augmentation, an important technique for image classification and object detection. We next moved on to handling video; we show how to form image frames from a video. Next, this chapter covered audio files; we formed a time series and mel spectrogram from an audio file. Finally, we moved on to cloud platforms and discussed the features and services provided by three major cloud service providers.
Hands-On Artificial Intelligence for IoT - Second Edition
By :
Hands-On Artificial Intelligence for IoT - Second Edition
By:
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
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
Other Books You May Enjoy
Index
Customer Reviews