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)
Heuristic Search Techniques and Logical Inference

In this chapter, we will introduce a broad range of problem-solving tools. We will start by looking at ontologies and knowledge-based reasoning before moving on to optimization in the context of Boolean satisfiability (SAT) and combinatorial optimization, where we'll simulate the result of individual behavior and coordination in society. Finally, we'll implement Monte Carlo tree search to find the best moves in chess.

We'll be dealing with various techniques in this chapter, including logic solvers, graph embeddings, genetic algorithms (GA), particle swarm optimization (PSO), SAT solvers, simulated annealing (SA), ant colony optimization, multi-agent systems, and Monte Carlo tree search.

In this chapter, we will cover the following recipes:

  • Making decisions based on knowledge
  • Solving the n-queens problem
  • Finding...