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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Visualizing the evolution

Let's see how to visualize the evolution process. In DEAP, there is a method called Covariance Matrix Adaptation Evolution Strategy (CMA-ES) to visualize evolutions. It is an evolutionary algorithm that's used to solve non-linear problems in the continuous domain. The CMA-ES technique is robust, well studied, and is considered "state-of-the-art" in evolutionary algorithms. Let's see how it works by delving into the source code. The following code is a slight variation of the example shown in the DEAP library.

Create a new Python file and import the following:

import numpy as np
import matplotlib.pyplot as plt
from deap import algorithms, base, benchmarks, \
        cma, creator, tools

Define a function to create the toolbox. We will define a FitnessMin function using negative weights:

# Function to create a toolbox
def create_toolbox(strategy):
    creator.create("FitnessMin", base.Fitness, weights...