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Introduction to deep learning

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Introduction to Deep Learning by Zeynep Kurultay from Algorithmia

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Introduction to deep learning

  1. 1. INTRODUCTIONTO DEEP LEARNING Zeynep Su Kurultay
  2. 2. Outline ■ Modeling humans in machines ■ Introduction to neural nets ■ What makes an algorithm intelligent? ■ Learning – Supervised learning ■ Deep learning – Neural nets in detail ■ Framework discussion & sample code ■ Future
  3. 3. Modeling humans in machines
  4. 4. Modeling humans in machines But why?
  5. 5. Neural networks ■ The mammal brain is organized in a deep architecture (Serre, Kreiman, Kouh, Cadieu, Knoblich, & Poggio, 2007) (E.g. visual system has 5 to 10 levels) ■ Very popular at the beginning of 1990s but fell out of favor after it was found that they were not performing well ■ Why is it gaining power again now: Deep architectures might be able to represent some functions otherwise not efficiently representable. Breakthrough in 2006/2007 with Hinton, Bengio papers
  6. 6. Examples around us
  7. 7. Examples around us Date: November 2014
  8. 8. Examples around us
  9. 9. Examples around us
  10. 10. Examples around us
  11. 11. Examples around us Image: NasenSpray/Imgur
  12. 12. Examples around us Image: http://www.telegraph.co.uk/technology/google/11730050/deep-dream-best-images.html?frame=3370388
  13. 13. Examples around us Image: drkaugumon/Imgur
  14. 14. What makes an algorithm intelligent? Image courtesy ofToptal.com
  15. 15. What makes an algorithm intelligent?
  16. 16. Learning ■ Supervised machine learning:The program is “trained” on a pre-defined set of “training examples”, which then facilitate its ability to reach an accurate conclusion when given new data. ■ Semi-supervised machine learning:The program infers the unknown labels through “label propagation”, utilizing similarities between different examples and inferring non-existent labels from existent ones ■ Unsupervised machine learning:The program is given a bunch of data and must find patterns and relationships therein. – e.g. clustering via nearest neighbor algorithm
  17. 17. Supervised Learning ■ Binary classification: Does this person have that disease? ■ Regression:What is the market value of this house? ■ Multiclass classification: Digit recognition, Face recognition
  18. 18. Supervised Learning ■ Goal: Given a number of features, try to make sense out of it! ■ Example: Employee satisfaction rates – depends on ?  So, given these features in a dataset, try to predict the rate
  19. 19. Supervised Learning
  20. 20. Supervised Learning
  21. 21. Supervised Learning
  22. 22. Supervised Learning
  23. 23. Supervised Learning ■ But how do we adjust ourselves? How do we know at each step we are getting better? ■ Measurement of wrongness: Loss functions
  24. 24. Loss functions
  25. 25. Gradient descent How do we know how to “roll down the hill”? The gradient (the derivatives of the loss function over all of the individual weights of features -i.e. parameters-) tells us “which way is down”.
  26. 26. What exactly is deep learning? ■ “a network would need more than one hidden layer to be a deep network, networks with one or two hidden layers are traditional neural networks…….” ■ “in my experience, a network can be considered deep when there is at least one hidden layer.Although the term deep learning can be fuzzy, …” ■ “in my own thinking, deep is not related to the number of layers, but it talks about how hard the feature to be discovered is…….” ■ - a discussion from StackExchange
  27. 27. Deep learning ■ What is the difference? Remember the quote fromYann LeCun from before? It goes on: ■ “A pattern recognition system is like a black box with a camera at one end, a green light and a red light on top, and a whole bunch of knobs on the front…. Now, imagine a box with 500 million knobs, 1,000 light bulbs, and 10 million images to train it with. That’s what a typical Deep Learning system is.”
  28. 28. Aim: Learning features ■ Deep learning excels in tasks where the basic unit, a single pixel, a single frequency, or a single word has very little meaning in and of itself, but the combination of such units has a useful meaning. It can learn these useful combinations of values without any human intervention.
  29. 29. Aim: Learning features (convolutional neural networks)
  30. 30. Neural networks ■ An input, output, and one or more hidden layers of units/neurons/perceptrons ■ Each connection between two neurons has a weight w. Best weights can again be found with gradient descent. Image courtesy of http://ljs.academicdirect.org/A15/053_070.htm
  31. 31. Neural networks ■ Example: Input vector: [7, 1, 2]  Into the input units ■ Forward propagation ■ Activation function Image courtesy of http://ljs.academicdirect.org/A15/053_070.htm
  32. 32. Neural networks ■ Why deep? ■ Number of parameterized transformations a signal encounters as it propagates from the input layer to the output layer, where a parameterized transformation is a processing unit that has trainable parameters, such as weights. Image courtesy of http://ljs.academicdirect.org/A15/053_070.htm
  33. 33. Aim: Learning features ■ The goal of deep learning methods is to learn higher levels of feature from lower level features.
  34. 34. Other important concepts ■ Overfitting – there is such a thing as learning too much –or too specific-! ■ Regularization – a technique that prevents overfitting
  35. 35. Overfitting ■ Overfitting – there is such a thing as learning too much –or too specific-! ■ Regularization – a technique that prevents overfitting
  36. 36. Overfitting U.S. Census Population overTime
  37. 37. Different frameworks ■ Pylearn2, Lasagne,Caffe,Torch,Theano, Blocks, Plate,Crino,Theanet, DL4J, Keras, …
  38. 38. Different frameworks ■ Theano: – A mathematical expression compiler, designed with machine learning in mind. – Lets you define an objective and automatically produces the code that computes the gradient of the objective. – Good for experimenting with different loss functions – Slightly lower layer of abstraction vs more possibilities
  39. 39. Different frameworks ■ Caffe: – Developed by UC Berkeley – Widely used machine-vision library that ported Matlab’s implementation of fast convolutional nets to C and C++ – Not intended for other deep-learning applications such as text, sound or time series data CORRECTION: There are new implementations of RNNs and LSTMs in Caffe, so it is not only for images any more! – Very fast: over 60M images per day with a single NVIDIA K40 GPU
  40. 40. Different frameworks ■ Torch: – Written in Lua (a scripting language developed in Brazil in the early 1990s) – A highly customized version of it is used by large tech companies such as Google and Facebook
  41. 41. Different frameworks ■ Keras: – Minimalist, highly modular neural network library in the spirit ofTorch – Written in Python – UsesTheano under the hood for optimized tensor manipulation on GPU andCPU – It was developed with a focus on enabling fast experimentation – 60K images took 30 hours on Amazon g2.2xlarge 
  42. 42. Comparing Keras andTheano MNIST digits dataset - serves as a benchmark to compare results with as new articles come out. Multilayer Perceptron - Basic feedforward neural network
  43. 43. Demo Code snippets – inside the gradient descent Output =Wx+b
  44. 44. Demo Code snippets – inside the hidden layer
  45. 45. Demo Code snippets – inside the hidden layer
  46. 46. Demo Code snippets – inside the hidden layer
  47. 47. Demo Code snippets – inside the network
  48. 48. Demo ■ https://algorithmia.com/demo/handwriting
  49. 49. Future of deep learning ■ Deep learning has a lot of hype right now, and it is apparent that it is very useful for specific tasks. ■ What frontiers and challenges do you think are the most exciting for researchers in the field of neural networks in the next ten years? ■ I cannot see ten years into the future. For me, the wall of fog starts at about 5 years. ... I think that the most exciting areas over the next five years will be really understanding videos and text. I will be disappointed if in five years time we do not have something that can watch aYouTube video and tell a story about what happened. I have had a lot of disappointments. – From Geoffrey Hinton’s AMA on Reddit
  50. 50. Now &The future Facebook Deep Learning, March 26, 2015 Image courtesy ofVenturebeat.com
  51. 51. Join us! ■ Open positions: https://angel.co/algorithmia/jobs/ – Algorithm Developer [this is me!] – Backend Developer – Product Manager – Technical Evangelist
  52. 52. Further resources ■ Introductory: ■ Andrew Ng’s Machine Learning course on Coursera ■ Geoffrey Hinton’s Neural Networks course on Coursera ■ Advanced: ■ Stanford’s Convolutional Neural Networks forVisual Recognition http://cs231n.github.io/ ■ Who is afraid of non-convex loss functions? ByYann LeCun http://videolectures.net/eml07_lecun_wia/ ■ What is wrong with Deep Learning? ByYann Lecun http://techtalks.tv/talks/whats-wrong-with-deep-learning/61639/ ■ For those who like papers, recent advances: ■ PlayingAtari with Deep Reinforcement Learning - http://www.cs.toronto.edu/~vmnih/docs/dqn.pdf ■ Unsupervised Face Detection - http://cs.stanford.edu/~quocle/faces_full.pdf
  53. 53. ■ Content: ■ Toptal.com, Deeplearning.net ■ http://www.computerworld.com/article/2918161/emerging-technology/the-ai-ecosystem.html ■ Introduction to Machine Learning CMU-10701 - Deep Learning slides ■ Images: ■ http://www.spyemporium.com/images/products/st-sc1720.jpg ■ http://stats.stackexchange.com/questions/128616/whats-a-real-world-example-of-overfitting ■ http://www.homedepot.com/catalog/productImages/1000/c4/c4c34d2e-56ce-4c11-94c0-67aa19b769fa_1000.jpg ■ http://www.bulborama.com/images/products/1933.jpg ■ https://xkcd.com/1122/, https://xkcd.com/1425/ ■ www.deeplearning.net

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