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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Recurrent Neural Networks and Other Deep Learning Models

In this chapter, we are going to learn about deep learning and Recurrent Neural Networks (RNNs). Like CNNs covered in previous chapters, RNNs have also gained a lot of momentum over the last few years. In the case of RNNs, they are heavily used in the area of speech recognition. Many of today's chatbots have built their foundation on RNN technologies. There has been some success predicting financial markets using RNNs. As an example, we might have a text with a sequence of words, and we have an objective to predict the next word in the sequence.

We will discuss the architecture of RNNs and their components. We will continue using TensorFlow, which we started learning about in the previous chapter. We will use TensorFlow to quickly build RNNs. We will also learn how to build an RNN classifier using a single layer neural network. We will then build an image classifier using a CNN.

By the end of this chapter...