Book Image

Artificial Intelligence for IoT Cookbook

By : Michael Roshak
Book Image

Artificial Intelligence for IoT Cookbook

By: Michael Roshak

Overview of this book

Artificial intelligence (AI) is rapidly finding practical applications across a wide variety of industry verticals, and the Internet of Things (IoT) is one of them. Developers are looking for ways to make IoT devices smarter and to make users’ lives easier. With this AI cookbook, you’ll be able to implement smart analytics using IoT data to gain insights, predict outcomes, and make informed decisions, along with covering advanced AI techniques that facilitate analytics and learning in various IoT applications. Using a recipe-based approach, the book will take you through essential processes such as data collection, data analysis, modeling, statistics and monitoring, and deployment. You’ll use real-life datasets from smart homes, industrial IoT, and smart devices to train and evaluate simple to complex models and make predictions using trained models. Later chapters will take you through the key challenges faced while implementing machine learning, deep learning, and other AI techniques, such as natural language processing (NLP), computer vision, and embedded machine learning for building smart IoT systems. In addition to this, you’ll learn how to deploy models and improve their performance with ease. By the end of this book, you’ll be able to package and deploy end-to-end AI apps and apply best practice solutions to common IoT problems.
Table of Contents (11 chapters)

Dev kits  

Often, companies have their devices designed by electrical engineers. This is a cost-effective option. Custom boards do not have extra components, such as unnecessary Bluetooth or extra USB ports. However, predicting CPU and RAM requirements of an ML model at board design time is difficult. Starter kits can be useful tools to use until the hardware requirements are understood. The following boards are among the most widely adopted boards on the market:

  • Manifold 2-C with NVIDIA TX2
  • The i.MX series
  • LattePanda
  • Raspberry Pi Class
  • Arduino
  • ESP8266

They are often used as a scale of functionality. A Raspberry Pi Class device, for example, would struggle with custom vision applications but would do great for audio or general ML applications. One determining factor for many data scientists is the programming language. The ESP8266 and Arduino need to be programmed in a low-level language such as C or C++, while devices such as Raspberry Pi Class or above can be programmed...