You're reading from Artificial Intelligence with Python Your complete guide to building intelligent apps using Python 3.x

Product type Paperback

Published in Jan 2020

Publisher Packt

ISBN-13 9781839219535

Length 618 pages

Edition 2nd Edition

Languages

Python

Tools

TensorFlow

Concepts

Artificial Intelligence

Authors (2):

Prateek Joshi

Alberto Artasanchez

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Table of Contents (26) Chapters

Preface

1. Introduction to Artificial Intelligence

2. Fundamental Use Cases for Artificial Intelligence FREE CHAPTER

3. Machine Learning Pipelines

4. Feature Selection and Feature Engineering

5. Classification and Regression Using Supervised Learning

6. Predictive Analytics with Ensemble Learning

7. Detecting Patterns with Unsupervised Learning

8. Building Recommender Systems

9. Logic Programming

10. Heuristic Search Techniques

11. Genetic Algorithms and Genetic Programming

12. Artificial Intelligence on the Cloud

13. Building Games with Artificial Intelligence

14. Building a Speech Recognizer

15. Natural Language Processing

16. Chatbots

17. Sequential Data and Time Series Analysis

18. Image Recognition

19. Neural Networks

20. Deep Learning with Convolutional Neural Networks

21. Recurrent Neural Networks and Other Deep Learning Models

22. Creating Intelligent Agents with Reinforcement Learning

23. Artificial Intelligence and Big Data

24. Other Books You May Enjoy

25. Index

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...