You're reading from Artificial Intelligence By Example Acquire advanced AI, machine learning, and deep learning design skills

Product type Paperback

Published in Feb 2020

Publisher Packt

ISBN-13 9781839211539

Length 578 pages

Edition 2nd Edition

Languages

Python

Tools

TensorFlow

Concepts

Artificial Intelligence

Author (1):

Denis Rothman

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Table of Contents (23) Chapters

Preface

1. Getting Started with Next-Generation Artificial Intelligence through Reinforcement Learning

2. Building a Reward Matrix – Designing Your Datasets FREE CHAPTER

3. Machine Intelligence – Evaluation Functions and Numerical Convergence

4. Optimizing Your Solutions with K-Means Clustering

5. How to Use Decision Trees to Enhance K-Means Clustering

6. Innovating AI with Google Translate

7. Optimizing Blockchains with Naive Bayes

8. Solving the XOR Problem with a Feedforward Neural Network

9. Abstract Image Classification with Convolutional Neural Networks (CNNs)

10. Conceptual Representation Learning

11. Combining Reinforcement Learning and Deep Learning

12. AI and the Internet of Things (IoT)

13. Visualizing Networks with TensorFlow 2.x and TensorBoard

14. Preparing the Input of Chatbots with Restricted Boltzmann Machines (RBMs) and Principal Component Analysis (PCA)

15. Setting Up a Cognitive NLP UI/CUI Chatbot

16. Improving the Emotional Intelligence Deficiencies of Chatbots

17. Genetic Algorithms in Hybrid Neural Networks

18. Neuromorphic Computing

19. Quantum Computing

20. Answers to the Questions

21. Other Books You May Enjoy

22. Index

Adding an SVM function

The self-driving car has delivered its packages to the shelters. Now it has to find a parking lot and park there. Instead of having a base like many other systems, this saves the city the cost of many useless trips.

Motivation – using an SVM to increase safety levels

The support vector system adds a new function to itinerary calculations—safety.

Most systems, such as Google Maps, focus on:

The shortest trip
The fastest trip
Traffic

However, self-driving cars have to take extra precautions. Many humans do not feel secure on some roads. Safety comes first, no matter what. Once a suitable parking lot has been found, the SVM has to avoid traffic.

The goal is to find a path through traffic, even if the distance is longer. A p parameter allows for a p% variance in the distance. For example, 10% allows a 10% longer distance and will provide safe passage, as shown in the following SVM result: