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

The basics of Recurrent Neural Networks

RNNs are another type of popular model that is currently gaining a lot of traction. As we discussed in Chapter 1, Introduction to Artificial Intelligence, the study of neural networks in general and RNNs in particular is the domain of the connectionist tribe (as described in Pedro Domingos' AI classification). RNNs are frequently used to tackle Natural Language Processing (NLP) and Natural Language Understanding (NLU) problems.

The math behind RNNs can be overwhelming at times. Before we get into the nitty gritty of RNNs, keep this thought in mind: a race car driver does not need to fully understand the mechanics of their car to make it go fast and win races. Similarly, we don't necessarily need to fully understand how RNNs work under the hood to make them do useful and sometimes impressive work for us. Francois Chollet, the creator of the Keras library, describes Long Short-Term Memory (LSTM) networks – which are a form...