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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A language modeling use case

Our goal is to build a language model using an RNN. Here's what that means. Let's say we have a sentence of m words. A language model allows us to predict the probability of observing the sentence (in a given dataset) as:

In words, the probability of a sentence is the product of probabilities of each word given the words that came before it. So, the probability of the sentence "Please let me know if you have any questions" would be the probability of "questions" given "Please let me know if you have any..." multiplied by the probability of "any" given "Please let me know if you have..." and so on.

How is that useful? Why is it important to assign a probability to the observation of a given sentence?

First, a model like this can be used as a scoring mechanism. A language model can be used to pick the most probable next word. Intuitively, the most probable next word is likely...