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)
24
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25
Index

Building a vector quantizer

Vector Quantization is a quantization technique where the input data is represented by a fixed number of representative points. It is the N-dimensional equivalent of rounding off a number. This technique is commonly used in multiple fields such as voice/image recognition, semantic analysis, and image/voice compression. The history of optimal vector quantization theory goes back to the 1950s in Bell Labs, where research was carried out to optimize signal transmission using discretization procedures. One advantage of vector quantizer neural networks is that they have high interpretability. Let's see how we can build a vector c.

Due to some issues with the current version of NeuroLab (v. 0.3.5), running the following code will throw an error. Fortunately, there is a fix for this, but it involves making a change in the NeuroLab package. Changing line 179 of the net.py file in the NeuroLab package (layer_out.np['w'][n][st:i].fill...