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

View More author details

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

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...