The document explains the distinctions between artificial intelligence, machine learning, and deep learning, highlighting their applications and functionalities. Machine learning identifies patterns in data to inform decisions, while deep learning utilizes neural networks for complex tasks without manual coding. The summary emphasizes that while machine learning is valuable, it is not always the optimal solution for every problem.

1. Title
Content
What is NOT Machine
Learning
Burak Aybar
b.aybar@visuality.pl

2. Introduction

3. Introduction

4. Introduction
Differences should be known—examples of AI, machine learning, and deep
learning are everywhere
● How Netflix knows which show you’ll want to watch next ?
● How Facebook knows whose face is in a photo ?
● What makes self-driving cars a reality ?
● How a customer service representative will know if you’ll be satisfied with
their support before you even take a customer satisfaction survey ?

5. What is Artificial Intelligence?

6. What is Artificial Intelligence?
The simulation of human intelligence processes by machines, especially
computer systems
The ability of machines to work and think, like the human brain
Applications
• Computer Vision
• Robotics
• Machine Learning
• ….

7. Examples of Artificial Intelligence

8. What is Deep Learning?

9. What is Deep Learning?
Deep Learning is a newer area of Machine Learning that uses multi-layered
artificial neural networks to deliver high accuracy in tasks such as object
detection, speech recognition, language translation and so on
Automatically learn/extract/translate the features from data sets such as
images, videos, text without introducing traditional hand-coded code or rules

10. What is Deep Learning?

11. What is Machine Learning?

12. What is Machine Learning ?
Machine learning is a branch of AI which finds patterns in data, learn from that
data, and then apply what they have learned to make informed decisions
Basically
Machine Learning is the science of getting computers to act without being
explicitly programmed

13. Examples of Machine Learning

14. Types of Machine Learning

15. Machine Learning vs Deep Learning

16. Machine Learning vs AI
Let's imagine we're building a self-driving car and trying to make it stop at
stop signs
Machine Learning: Create a dataset with
millions of street-side objects photos and
train an algorithm to recognise which
have stop signs on them
Artificial Intelligence: As soon as the car
recognises stop signs, it should start
applying the brakes. The car should hit the
brakes right in time, not too early or too late.

17. Summary

18. Summary
Machine learning uses the experience to
look for the pattern it learned. Artificial
Intelligence uses the experience to
acquire knowledge/skill and also how to
apply that knowledge for new
environments
Deep Learning is a subclass of Machine
Learning, basically, neural networks that use
multiple hidden layers. Their complexity
allows this type of algorithms to perform
feature extraction on their own

19. Summary
Given the usefulness of machine learning, it can be hard to accept that sometimes
it is not the best solution to a problem

20. Title
Content
Thank you!
Burak Aybar
b.aybar@visuality.pl

21. References

22. References
https://blogs.nvidia.com/
https://vas3k.com/
https://quantdare.com/

23. Demo

24. Demo
https://youtu.be/Face2Face
https://youtu.be/Baby-X
https://drumbot.glitch.me