Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Neuro-Symbolic AI

You're reading from   Neuro-Symbolic AI Design transparent and trustworthy systems that understand the world as you do

Arrow left icon
Product type Paperback
Published in May 2023
Publisher Packt
ISBN-13 9781804617625
Length 196 pages
Edition 1st Edition
Concepts
Arrow right icon
Authors (2):
Arrow left icon
Alexiei Dingli Alexiei Dingli
Author Profile Icon Alexiei Dingli
Alexiei Dingli
David Farrugia David Farrugia
Author Profile Icon David Farrugia
David Farrugia
Arrow right icon
View More author details
Toc

Table of Contents (12) Chapters Close

Preface 1. Chapter 1: The Evolution and Pitfalls of AI 2. Chapter 2: The Rise and Fall of Symbolic AI FREE CHAPTER 3. Chapter 3: The Neural Networks Revolution 4. Chapter 4: The Need for Explainable AI 5. Chapter 5: Introducing Neuro-Symbolic AI – the Next Level of AI 6. Chapter 6: A Marriage of Neurons and Symbols – Opportunities and Obstacles 7. Chapter 7: Applications of Neuro-Symbolic AI 8. Chapter 8: Neuro-Symbolic Programming in Python 9. Chapter 9: The Future of AI 10. Index 11. Other Books You May Enjoy

The rise of data

Data refers to the raw facts and figures that are collected and processed to provide useful information. In this context, data is essential for training models to recognize patterns and make predictions. The rise of data has been closely linked with the growth of the internet. As more people have come online and generated more information, the amount of data available has grown exponentially. This has had a profound impact on deep learning, enabling researchers to develop increasingly sophisticated models that can learn from vast amounts of data. As more people came online, companies began to realize the value of this data. They started collecting it en masse, using it to gain insights into customer behavior and preferences. This led to the emergence of big data – large datasets that were too complex to be processed using traditional methods. Big data presented a challenge for machine learning researchers. Traditional machine learning algorithms were not designed...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image