Deep learning beyond_the_hype-franceia@lapaillasse
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Presentation of deep learning, some of its potential and adverse examples.
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Deep learning beyond_the_hype-franceia@lapaillasse
- 1. Deep Learning, beyond the hype! 3 mars 2017, La Paillasse Sébastien TREGUER
- 2. Machine Learning vs Deep learning vs A.I.
- 3. What are the different species in the zoo?
- 5. Performances 2012: from 26% to 16% error rate in image classification with convolutional neural nets Similar disruption observed in speech
- 6. What’s the secret sauce of Deep Neural Nets ? ● RMSProp ● ADAM ● SGD ● Sigmoid ● Tanh ● ReLU ● Leaky ReLU ● CNN ● RNN ● LSTM ● GAN ● WaveNet ● PathNet The more, the cleaner and well distributed the better
- 7. How does it works? From details captured in the first layers to higher level of abstraction in deeper layers
- 9. What about creativity? Representation Learning Reinforcement Learning, Alphago learning by playing against itself Music improvisation Logic operations on images
- 10. Input = “A small yellow bird with a black crown and a short black pointed beak” Which images are real, which images are generated by the algorithm? From classification to generation
- 11. A small yellow bird with a back crown and a short black pointed beak From classification to generation Answer: None of these images ever existed. They are all generated by the algorithm. StackGAN, Han Zhang & al, paper here
- 12. GAN Generative Adversarial Networks Ian J. Goodfellow & al, 2014 StackGAN Text to Photo-realistic Image Synthesis Han Zhang & al, 2016 From classification to generation
- 13. Adversarial examples “Panda” 57.7% Confidence “Nematode” 8.2% Confidence “Gibbon” 99.9% Confidence Any Limits?
- 14. Unsurpervised learning Reinforcement Learning → predict a scalar reward → A few bits per samples Supervised Learning = on-sight navigation → Predicting human supplied data → 10 to 10000 bits per samples All the rest is unsupervised Learning = offshore navigation → Predicting unknown parts from observation → Building its own representation → Millions of bits per samples Ex: predicting frames in videos, generating images, music... Any Limits?
- 15. Energy efficiency? VS Brain = 20W 2.2 billion megaflops Super Computers = until 10 millions watts The race for power