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)
Artificial Intelligence in Production

In the creation of a system that involves artificial intelligence (AI), the actual AI usually only takes a small fraction of the total amount of work, while a major part of the implementation entails the surrounding infrastructure, starting from data collection and verification, feature extraction, analysis, resource management, and serving and monitoring (David Sculley and others. Hidden technical debt in machine learning systems, 2015).

In this chapter, we'll deal with monitoring and model versioning, visualizations as dashboards, and securing a model against malicious hacking attacks that could leak user data.

In this chapter, we'll be covering the following recipes:

  • Visualizing model results
  • Serving a model for live decisioning
  • Securing a model against attack