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)

Importing TensorFlow

Perhaps the easiest way to import a Python library such as TensorFlow is to use PyPI. Simply go to https://pypi.org/ and search for TensorFlow. This will give you the information needed and the ability to look at different versions. The installation steps are as follows:

  1. Go to https://pypi.org/ and search for TensorFlow.
  2. Copy the name and version number you want in this format: tensorflow==1.14.0
  3. In the Workspace tab of Databricks, right-click anywhere and from the dropdown, click on Create and then Library:

  1. On the Create Library page, select PyPI as the library source:

  1. Copy the name of the library and the version number and paste that into the Package section.
  2. Click Create.

If you have already created a cluster, you can attach TensorFlow to it. You can also have TensorFlow installed on all clusters.