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Presentation dl beyond-the_hype-v0.3

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Ma présentation sur le "Deep Learning, au delà du phénomène de hype", le vendredi 3 mars 2017, à La Paillasse dans le cadre des journées de l'I.A., entre une intervention de Guillaume Dumas, sur "Comment les neurosciences questionnent l'intelligence artificielle"

Publié dans : Données & analyses
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Presentation dl beyond-the_hype-v0.3

  1. 1. Deep Learning, beyond the hype!
  2. 2. Machine Learning vs Deep learning vs A.I.
  3. 3. What are the different species in the zoo?
  4. 4. Hype or reality?
  5. 5. Performances
  6. 6. What’s the secret sauce of Deep Neural Nets ?
  7. 7. How does it works?
  8. 8. Depth and Performances
  9. 9. What about creativity?
  10. 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. 11. From classification to generation A small yellow bird with a back crown and a short black pointed beak
  12. 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. 13. Any Limits? Adversarial examples “Panda” 57.7% Confidence “Nematode” 8.2% Confidence “Gibbon” 99.9% Confidence
  14. 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. 15. The race for power Energy efficiency? VS Brain = 20W 2.2 billion megaflops Super Computers = until 10 millions watts
  16. 16. Annexe: Stack GAN

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