Book Image

Artificial Intelligence with Python - Second Edition

By : Prateek Joshi
Book Image

Artificial Intelligence with Python - Second Edition

By: Prateek Joshi

Overview of this book

Artificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications. This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data. Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques.
Table of Contents (26 chapters)
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Solving the symbol regression problem

We will see at the end of this chapter the many applications of GAs to a vast amount of industries and domains. From finance to traffic optimization, the applications of GAs are almost endless. For now, though, we continue with another simple example. Let's see how to use genetic programming to solve the symbol regression problem. It is important to understand that genetic programming is not the same as GAs. Genetic programming is a type of evolutionary algorithm in which the solutions occur in the form of computer programs. The individuals in each generation would be computer programs and their fitness level correspond to their ability to solve problems. These programs are modified, at each iteration, using a GA. Genetic programming is the application of a GA.

Coming to the symbol regression problem, we have a polynomial expression that needs to be approximated here. It's a classic regression problem where we try to estimate the underlying...