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

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...