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![[Kudos to Richard Socher, for this eloquent summary :) ]
• Manually designed features are often over-specified, incomplete
and take a long time to design and validate
• Learned Features are easy to adapt, fast to learn
• Deep learning provides a very flexible, (almost?) universal,
learnable framework for representing world, visual and
linguistic information.
• Deep learning can learn unsupervised (from raw text/audio/
images/whatever content) and supervised (with specific labels
like positive/negative)
Why Deep Learning ?](https://image.slidesharecdn.com/20415dlbirdseyeview-150420223125-conversion-gate02/85/Deep-Learning-a-birds-eye-view-67-320.jpg)
Uploaded byRoelof Pieters
Deep Learning: a birds eye view
Deep learning is a type of machine learning that uses neural networks with multiple layers between the input and output layers. It allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. Deep learning has achieved great success in computer vision, speech recognition, and natural language processing due to recent advances in algorithms, computing power, and the availability of large datasets. Deep learning models can learn complex patterns directly from large amounts of unlabeled data without relying on human-engineered features.
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Deep Learning: a birds eye view
- 1. @graphific Roelof Pieters Deep Learning: a(non-techy) birds-eye view 20 April 2015 Stockholm Deep Learning Slides at: http://www.slideshare.net/roelofp/deep-learning-a-birdseye-view
- 2. • Deals with“construction and study of systems that can learn from data” Refresher: Machine Learning ??? A computer program is said to learn from experience (E) with respect to some class of tasks (T) and performance measure (P), if its performance at tasks in T, as measured by P, improves with experience E — T. Mitchell 1997 2
- 3. Improving some taskT based on experience E with respect to performance measure P. Deep Learning = Machine Learning Learning denotes changes in the system that are adaptive in the sense that they enable the system to do the same task (or tasks drawn from a population of similar tasks) more effectively the next time. — H. Simon 1983 "Why Should Machines Learn?” in Mitchell 1997 — T. Mitchell 1997 3
- 4. Representation learning Attempts toautomatically learn good features or representations Deep learning Attempt to learn multiple levels of representation of increasing complexity/abstraction Deep Learning: What? 4
- 5.
- 6. Machine Learning ?? TraditionalProgramming: Data Program Output 6 Computer
- 7. Machine Learning ?? TraditionalProgramming: Data Program Output Data Program Output Machine Learning: 7 (labels) (“weights”/model) Computer Computer
- 8. Machine Learning ?? 8 •Most machine learning methods work well because of human-designed/hand- engineered features (representations) • machine learning -> optimising weights to best make a final prediction
- 9. Typical ML Regression DeepLearning: Why?
- 10. Neural NetTypical MLRegression Deep Learning: Why?
- 11.
- 12. Machine ->Deep Learning?? DEEP NET (DEEP LEARNING)
- 14.
- 15.
- 16. Deep Learning iseverywhere…
- 18. Deep Learning inthe News
- 19.
- 20. (source: arXiv bookworm,Culturomics) Scientific Articles:
- 25. Why Now? • Inspiredby the architectural depth of the brain, researchers wanted for decades to train deep multi-layer neural networks. • No successful attempts were reported before 2006 …Exception: convolutional neural networks, LeCun 1998 • SVM: Vapnik and his co-workers developed the Support Vector Machine (1993) (shallow architecture). • Breakthrough in 2006! 25
- 26. Renewed Interest: 1990s •Learning multiple layers • “Back propagation” • Can “theoretically” learn any function! But… • Very slow and inefficient • SVMs, random forests, etc. SOTA 26
- 27. 2006 Breakthrough • Moredata • Faster hardware: GPU’s, multi-core CPU’s • Working ideas on how to train deep architectures 27
- 28. 2006 Breakthrough • Moredata • Faster hardware: GPU’s, multi-core CPU’s • Working ideas on how to train deep architectures 28
- 29.
- 30. 2006 Breakthrough 30 Growth ofdatasets
- 31. 2006 Breakthrough • Moredata • Faster hardware: GPU’s, multi-core CPU’s • Working ideas on how to train deep architectures 31
- 32.
- 33. Rise of RawComputation Power
- 34. 2006 Breakthrough • Moredata • Faster hardware: GPU’s, multi-core CPU’s • Working ideas on how to train deep architectures 34
- 35. 2006 Breakthrough Stacked RestrictedBoltzman Machines* (RBM) Hinton, G. E, Osindero, S., and Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18:1527-1554. Stacked Autoencoders (AE) Bengio, Y., Lamblin, P., Popovici, P., Larochelle, H. (2007). Greedy Layer-Wise Training of Deep Networks, Advances in Neural Information Processing Systems 19 * called Deep Belief Networks (DBN) 35
- 36. Deep Learning forthe Win!
- 37. • 1.2M imageswith 1000 object categories • AlexNet of uni Toronto: 15% error rate vs 26% for 2th placed (traditional CV) Impact on Computer Vision ImageNet Challenge 2012
- 38. Impact on ComputerVision (from Clarifai)
- 39. Impact on ComputerVision
- 40. 40 Classification results onImageNet 2012 Team Year Place Error (top-5) Uses external data SuperVision 2012 - 16.4% no SuperVision 2012 1st 15.3% ImageNet 22k Clarifai 2013 - 11.7% no Clarifai 2013 1st 11.2% ImageNet 22k MSRA 2014 3rd 7.35% no VGG 2014 2nd 7.32% no GoogLeNet 2014 1st 6.67% no Final Detection Results Team Year Place mAP e x t e r n a l data ensemble c o n t e x t u a l model approach UvA-Euvision 2013 1st 22.6% none ? yes F i s h e r vectors Deep Insight 2014 3rd 40.5% I L S V R C 1 2 Classification + Localization 3 models yes ConvNet C U H K DeepID-Net 2014 2nd 40.7% I L S V R C 1 2 Classification + Localization ? no ConvNet GoogLeNet 2014 1st 43.9% I L S V R C 1 2 Classification 6 models no ConvNet Detection results source: Szegedy et al. Going deeper with convolutions (GoogLeNet ), ILSVRC2014, 19 Sep 2014
- 41. 41source: Szegedy etal. Going deeper with convolutions (GoogLeNet ), ILSVRC2014, 19 Sep 2014 GoogLeNet Convolution Pooling Softmax Other Winners of: Large Scale Visual Recognition Challenge 2014 (ILSVRC2014) 19 September 2014 GoogLeNet Convolution Pooling Softmax Other
- 42. 42source: Szegedy etal. Going deeper with convolutions (GoogLeNet ), ILSVRC2014, 19 Sep 2014 Inception Width of inception modules ranges from 256 filters (in early modules) to 1024 in top inception modules. Can remove fully connected layers on top completely Number of parameters is reduced to 5 million 256 480 480 512 512 512 832 832 1024 Computional cost is increased by less than 2X compared to Krizhevsky’s network. (<1.5Bn operations/evaluation)
- 43. Impact on ComputerVision Latest State of the Art:
- 44. Computer Vision: CurrentState of the Art
- 45. Impact on AudioProcessing 45 First public Breakthrough with Deep Learning in 2010 Dahl et al. (2010)
- 46. Impact on AudioProcessing 46 First public Breakthrough with Deep Learning in 2010 Dahl et al. (2010) -33%! -32%!
- 47. Impact on AudioProcessing 47 Speech Recognition
- 48. Impact on AudioProcessing 48 TIMIT Speech Recognition (from: Clarifai)
- 49. Impact on AudioProcessing
- 50. C&W 2011 Impact onNatural Language Processing Pos: Toutanova et al. 2003) Ner: Ando & Zhang 2005 C&W 2011
- 51. Impact on NaturalLanguage Processing Named Entity Recognition:
- 52. Deep Learning: Who’sto blame?
- 53. 53 Deep Learning: Who’sto blame?
- 54. Deep Architectures canbe representationally efficient • Fewer computational units for same function Deep Representations might allow for a hierarchy or representation • Allows non-local generalisation • Comprehensibility Multiple levels of latent variables allow combinatorial sharing of statistical strength 54 Deep Learning: Why?
- 55. — Andrew Ng “I’veworked all my life in Machine Learning, and I’ve never seen one algorithm knock over benchmarks like Deep Learning” Deep Learning: Why? 55
- 56. Biological Justification Deep Learning= Brain “inspired” Audio/Visual Cortex has multiple stages == Hierarchical
- 57. Different Levels ofAbstraction 57
- 58. Hierarchical Learning • Naturalprogression from low level to high level structure as seen in natural complexity Different Levels of Abstraction Feature Representation 58
- 59. Hierarchical Learning • Naturalprogression from low level to high level structure as seen in natural complexity• Easier to monitor what is being learnt and to guide the machine to better subspaces Different Levels of Abstraction Feature Representation 59
- 60. Hierarchical Learning • Naturalprogression from low level to high level structure as seen in natural complexity• Easier to monitor what is being learnt and to guide the machine to better subspaces • A good lower level representation can be used for many distinct tasks Different Levels of Abstraction Feature Representation 60
- 61. Hierarchical Learning • Naturalprogression from low level to high level structure as seen in natural complexity• Easier to monitor what is being learnt and to guide the machine to better subspaces • A good lower level representation can be used for many distinct tasks Different Levels of Abstraction Feature Representation 61
- 62. Different Levels ofAbstraction
- 63. Classic Deep Architecture Inputlayer Hidden layers Output layer
- 64. Modern Deep Architecture Inputlayer Hidden layers Output layer movie time: http://www.cs.toronto.edu/~hinton/adi/index.htm
- 65. Hierarchies Efficient Generalization Distributed Sharing Unsupervised* Black Box Training Time MajorPWNAGE! Much Data Why go Deep ? 65
- 66. No More HandcraftedFeatures ! 66
- 67. [Kudos to RichardSocher, for this eloquent summary :) ] • Manually designed features are often over-specified, incomplete and take a long time to design and validate • Learned Features are easy to adapt, fast to learn • Deep learning provides a very flexible, (almost?) universal, learnable framework for representing world, visual and linguistic information. • Deep learning can learn unsupervised (from raw text/audio/ images/whatever content) and supervised (with specific labels like positive/negative) Why Deep Learning ?
- 68. Deep Learning: FutureDevelopments Currently an explosion of developments • Hessian-Free networks (2010) • Long Short Term Memory (2011) • Large Convolutional nets, max-pooling (2011) • Nesterov’s Gradient Descent (2013) Currently state of the art but... • No way of doing logical inference (extrapolation) • No easy integration of abstract knowledge • Hypothetic space bias might not conform with reality 68
- 69. Deep Learning: FutureChallenges a 69 Szegedy, C., Wojciech, Z., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R. (2013) Intriguing properties of neural networks L: correctly identified, Center: added noise x10, R: “Ostrich”
- 72. as PhD candidateKTH/CSC: “Always interested in discussing Machine Learning, Deep Architectures, Graphs, and Language Technology” In touch! roelof@kth.se www.csc.kth.se/~roelof/ Data Science ConsultancyAcademic/Research roelof@gve-systems.com www.gve-systems.com 72 Gve Systems Graph Technologies
- 73. • Theano -CPU/GPU symbolic expression compiler in python (from LISA lab at University of Montreal). http://deeplearning.net/software/theano/ • Pylearn2 - library designed to make machine learning research easy. http://deeplearning.net/software/ pylearn2/ • Torch - Matlab-like environment for state-of-the-art machine learning algorithms in lua (from Ronan Collobert, Clement Farabet and Koray Kavukcuoglu) http://torch.ch/ • more info: http://deeplearning.net/software links/ Wanna Play ? Wanna Play ? General Deep Learning 73
- 74. • RNNLM (Mikolov) http://rnnlm.org •NB-SVM https://github.com/mesnilgr/nbsvm • Word2Vec (skipgrams/cbow) https://code.google.com/p/word2vec/ (original) http://radimrehurek.com/gensim/models/word2vec.html (python) • GloVe http://nlp.stanford.edu/projects/glove/ (original) https://github.com/maciejkula/glove-python (python) • Socher et al / Stanford RNN Sentiment code: http://nlp.stanford.edu/sentiment/code.html • Deep Learning without Magic Tutorial: http://nlp.stanford.edu/courses/NAACL2013/ Wanna Play ? NLP 74
- 75. • cuda-convnet2 (AlexKrizhevsky, Toronto) (c++/ CUDA, optimized for GTX 580) https://code.google.com/p/cuda-convnet2/ • Caffe (Berkeley) (Cuda/OpenCL, Theano, Python) http://caffe.berkeleyvision.org/ • OverFeat (NYU) http://cilvr.nyu.edu/doku.php?id=code:start Wanna Play ? Computer Vision 75