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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Neural Networks

In this chapter, we are going to learn about neural networks. We will start with an introduction to neural networks and the installation of the relevant library. We will then discuss perceptrons and how to build a classifier based on them. After that, we'll go deeper and learn about single-layer neural networks and multi-layer neural networks.

Later, we will see how to use neural networks to build a vector quantizer. We will analyze sequential data using recurrent neural networks, and finally we will use neural networks to build an optical character recognition engine. By the end of this chapter, we will have covered:

  • An introduction to neural networks
  • Building a Perceptron-based classifier
  • Constructing a single-layer neural network
  • Constructing a multilayer neural network
  • Building a vector quantizer
  • Analyzing sequential data using recurrent neural networks
  • Visualizing characters in an Optical Character Recognition ...