This document discusses deep learning and its applications. It explores the differences between machine learning, deep learning, and artificial intelligence. It then examines how deep neural networks work and how they can generate new images from text descriptions. The document notes some limitations of deep learning, such as adversarial examples, and discusses future areas of research like unsupervised and reinforcement learning. It closes by comparing the energy efficiency of the human brain to modern supercomputers.

1. Deep Learning, beyond the hype!

2. Machine Learning vs Deep learning
vs A.I.

3. What are the different species in the
zoo?

4. Hype or reality?

5. Performances

6. What’s the secret sauce of Deep
Neural Nets ?

7. How does it works?

8. Depth and Performances

9. What about creativity?

10. From classification to generation
A small yellow bird with a back crown and a short black pointed beak
Which ones are real images, which ones are generated by the algorithm?

11. From classification to generation
A small yellow bird with a back crown and a short black pointed beak

12. From classification to generation
GAN
Generative Adversarial Networks
Ian J. Goodfellow & al, 2014
StackGAN
Text to Photo-realistic Image Synthesis
Han Zhang & al, 2016

13. Any Limits?
Adversarial examples
“Panda”
57.7% Confidence
“Nematode”
8.2% Confidence
“Gibbon”
99.9% Confidence

14. Any Limits?
Unsurpervised learning
Reinforcement Learning
→ predict a scalar reward
→ A few bits per samples
Supervised Learning
→ Predicting human supplied data
→ 10 to 10000 bits per samples
All the rest is unsupervised Learning
→ Predicting unknown parts from observation
→ Building its own representation
→ Millions of bits per samples
Ex: predicting frames in videos, generating images, music...

15. The race for power
Energy efficiency?
VS
Brain = 20W
2.2 billion megaflops
Super Computers =
until 10 millions watts

16. Annexe: Stack GAN