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)
Getting Started with Artificial Intelligence in Python

In this chapter, we'll start by setting up a Jupyter environment to run our experiments and algorithms in, we'll get into different nifty Python and Jupyter hacks for artificial intelligence (AI), we'll do a toy example in scikit-learn, Keras, and PyTorch, and then a slightly more elaborate example in Keras to round things off. This chapter is largely introductory, and a lot of what see in this chapter will be built on in subsequent chapters as we get into more advanced applications.

In this chapter, we'll cover the following recipes:

  • Setting up a Jupyter environment
  • Getting proficient in Python for AI
  • Classifying in scikit-learn, Keras, and PyTorch
  • Modeling with Keras