Replicating the human brain: Deep learning in action
•
1 gostou•4,607 visualizações
Since the foundation of the AI field in 1956, researchers have been using the human brain as inspiration, and as a final goal to replicate. Recent developments in deep learning made a step forward on tasks that are "easy for a mind, and difficult for a computer" such as pattern recognition in images. In this talk we will cover the basics of the human brain, and artificial neural networks, and we will see what deep learning is, what it can be used for, and how it relates to the human brain.
Recomendados
Recomendados
Mais conteúdo relacionado
Semelhante a Replicating the human brain: Deep learning in action
Semelhante a Replicating the human brain: Deep learning in action (20)
Último
Último (20)
Replicating the human brain: Deep learning in action
- 1. Replicating the human brain: Deep learning in action Marc Garcia PyData Mallorca - January 25th, 2017 1 / 25 Replicating the human brain: Deep learning in action
- 2. About me http://datapythonista.github.io 2 / 25 Replicating the human brain: Deep learning in action
- 3. The human brain 3 / 25 Replicating the human brain: Deep learning in action
- 4. The human brain Visual perception example 4 / 25 Replicating the human brain: Deep learning in action
- 5. The human brain Visual perception 5 / 25 Replicating the human brain: Deep learning in action
- 6. The human brain Neuron synapse Neuron doctrine, Santiago Ramón y Cajal 6 / 25 Replicating the human brain: Deep learning in action
- 7. The human brain Hubel and Torsten cat experiment Receptive fields of single neurones in the cat’s striate cortex David H Hubel and Torsten N Wiesel, 1959, The Journal of physiology 7 / 25 Replicating the human brain: Deep learning in action
- 8. The human brain Hebbian theory Learning The capacity of a neuron to activate another changes over time Memory This capacity of activation can recall previous activations Let us assume that the persistence or repetition of a reverberatory activity (or "trace") tends to induce lasting cellular changes that add to its stability.[...] When an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A’s efficiency, as one of the cells firing B, is increased. The Organization of Behavior, Donald Hebb, 1949 8 / 25 Replicating the human brain: Deep learning in action
- 9. Deep learning: structure 9 / 25 Replicating the human brain: Deep learning in action
- 10. Deep learning: structure McCulloch-Pitts neuron x2 w2 Σ f Activate function y Output x1 w1 x3 w3 Weights Bias b Inputs A logical calculus of the ideas immanent in nervous activity Warren S. McCulloch and Walter Pitts, 1943, Bulletin of Mathematical Biophysics 10 / 25 Replicating the human brain: Deep learning in action
- 11. Deep learning: structure Activate function 11 / 25 Replicating the human brain: Deep learning in action
- 12. Deep learning: structure Hopfield networks Neural networks and physical systems with emergent collective computational abilities John J Hopfield, 1982, Proceedings of the National Academy of Sciences of the USA 12 / 25 Replicating the human brain: Deep learning in action
- 13. Deep learning: structure Hopfield networks 13 / 25 Replicating the human brain: Deep learning in action
- 14. Deep learning: structure Hopfield networks 14 / 25 Replicating the human brain: Deep learning in action
- 15. Deep learning: structure Boltzman machines Optimal Perceptual Inference Geoffrey E. Hinton and Terrence J. Sejnowski, 1983 Proceedings of the IEEE conference on Computer Vision and Pattern Recognition 15 / 25 Replicating the human brain: Deep learning in action
- 16. Deep learning: structure Restricted Boltzman machines Information Processing in Dynamical Systems: Foundations of Harmony Theory Paul Smolensky, 1986, Parallel Distributed Processing, Volume 1, Chapter 6 16 / 25 Replicating the human brain: Deep learning in action
- 17. Deep learning: structure Deep belief networks A fast learning algorithm for deep belief nets Yee-Whye Teh et al., 2006, Neural computation 17 / 25 Replicating the human brain: Deep learning in action
- 18. Deep learning: structure Deep belief networks 18 / 25 Replicating the human brain: Deep learning in action
- 19. Deep learning: training 19 / 25 Replicating the human brain: Deep learning in action
- 20. Deep learning: training Loss function Error in our predictions, compared to truth error = y − θ0 + θ1 · x1 (1) 20 / 25 Replicating the human brain: Deep learning in action
- 21. Deep learning: training Stochastic gradient descent 21 / 25 Replicating the human brain: Deep learning in action
- 22. Deep learning: training Backpropagation 22 / 25 Replicating the human brain: Deep learning in action
- 23. Deep learning: training Deep learning in practise Not for everyone, just makes sense at a scale Many layers to extract features Curse of dimensionality The key is to compute derivatives very fast: Theano Tensorflow Torch ... 23 / 25 Replicating the human brain: Deep learning in action
- 24. Deep learning: training Google Youtube experiment Building High-level Features Using Large Scale Unsupervised Learning Quoc V. Le, 2012, Proceedings of the 29th International Conference on ML 24 / 25 Replicating the human brain: Deep learning in action
- 25. Deep learning: training Questions? @datapythonista 25 / 25 Replicating the human brain: Deep learning in action