Book Image

Artificial Intelligence with Python Cookbook

By : Ben Auffarth
Book Image

Artificial Intelligence with Python Cookbook

By: Ben Auffarth

Overview of this book

Artificial intelligence (AI) plays an integral role in automating problem-solving. This involves predicting and classifying data and training agents to execute tasks successfully. This book will teach you how to solve complex problems with the help of independent and insightful recipes ranging from the essentials to advanced methods that have just come out of research. Artificial Intelligence with Python Cookbook starts by showing you how to set up your Python environment and taking you through the fundamentals of data exploration. Moving ahead, you’ll be able to implement heuristic search techniques and genetic algorithms. In addition to this, you'll apply probabilistic models, constraint optimization, and reinforcement learning. As you advance through the book, you'll build deep learning models for text, images, video, and audio, and then delve into algorithmic bias, style transfer, music generation, and AI use cases in the healthcare and insurance industries. Throughout the book, you’ll learn about a variety of tools for problem-solving and gain the knowledge needed to effectively approach complex problems. By the end of this book on AI, you will have the skills you need to write AI and machine learning algorithms, test them, and deploy them for production.
Table of Contents (13 chapters)

Discovering anomalies

An anomaly is anything that deviates from the expected or normal outcomes. Detecting anomalies can be important in Industrial Process Monitoring (IPM), where data-driven fault detection and diagnosis can help achieve achieve higher levels of safety, efficiency, and quality.

In this recipe, we'll look at methods for outlier detection. We'll go through an example of outlier detection in a time series with Python Outlier Detection (pyOD), a toolbox for outlier detection that implements many state-of-the-art methods and visualizations. PyOD's documentation can be found at

We'll apply an autoencoder for a similarity-based approach, and then an online learning approach suitable for finding events in streams of data.

Getting ready

This recipe will focus on finding outliers. We'll demonstrate how to do this with the pyOD library including an autoencoder approach. We'll also outline the upsides and downsides...