Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
Deep Learning
What’s the buzz all about?
Dr. Debdoot Sheet
Assistant Professor
Department of Electrical Engineering
Indian...
5 Aug. 2014 2Deep Learning - What's the buzz all about? / Debdoot Sheet
Learning?
A computer program is said to learn from
experience E with respect to some class of
tasks T and performance meas...
Demystifying Learning
5 Aug. 2014 Deep Learning - What's the buzz all about? / Debdoot Sheet 4
Man 1 Man 2 Man 3Man 4
Grea...
How was it Learning?
5 Aug. 2014 Deep Learning - What's the buzz all about? / Debdoot Sheet 5
Man 1 Man 2 Man 3Man 4
Great...
Challenges
5 Aug. 2014 Deep Learning - What's the buzz all about? / Debdoot Sheet 6
Salient Segments
Objectify
Detect
huma...
Challenges
5 Aug. 2014 Deep Learning - What's the buzz all about? / Debdoot Sheet 7
Salient Segments
Objectify
Recognize
i...
Dilemma only in Machine Vision?
• Speech Recognition and Signal Processing
– Dahl, et al,(2012). Context dependent pre-tra...
How to tackle this dilemma?
5 Aug. 2014 Deep Learning - What's the buzz all about? / Debdoot Sheet 9
Great Wall
behind
Gre...
Multilayer Perceptron (MLP)
5 Aug. 2014 Deep Learning - What's the buzz all about? / Debdoot Sheet 10
Hiddenlayers
Hiddenl...
MLP Learning, troubles thereof
5 Aug. 2014 Deep Learning - What's the buzz all about? / Debdoot Sheet 11
P
T1
T2
MLP Learning troubles, why so?
5 Aug. 2014 Deep Learning - What's the buzz all about? / Debdoot Sheet 12
P
T1
T2
LBP
Wavel...
MLP Learning troubles, why so?
5 Aug. 2014 Deep Learning - What's the buzz all about? / Debdoot Sheet 13
P
T1
T2
Chroma
cl...
MLP Learning troubles, why so?
5 Aug. 2014 Deep Learning - What's the buzz all about? / Debdoot Sheet 14
P
T1
T2
?
Deep Learning, origin and growth
• Around 1950 – NN age
– Neural Nets (McCulloch and Pitts,
1943)
– Unsupervised Learn. (H...
Deep Learning, origin and growth
• 1980-2000 – Search for simple,
low-complexity, problem-solvers
– Recurrent Neural Netwo...
Deep Learning, origin and growth
• 2000 – Era of Deep Learning
– NIPS 2003 Feature Selection
Challenge (Neal and Zhang, 20...
Science fiction or reality
Synthetic facial expression Machine that can dream
5 Aug. 2014 Deep Learning - What's the buzz ...
Deep Learning, where
• Facebook
• Baidu – IDL
• Cortica
• Cortexica
• Google
– DNNresearch
– Deep Mind
• Microsoft
– DNN
–...
COMPUTATIONAL
MEDICAL IMAGING
my experiences with
Deep Learning (or not) for
5 Aug. 2014 Deep Learning - What's the buzz a...
(Not so Deep) Transfer Learning
5 Aug. 2014 Deep Learning - What's the buzz all about? / Debdoot Sheet 21
D. Sheet, et al,...
Local Minima Juggling, experience
5 Aug. 2014 Deep Learning - What's the buzz all about? / Debdoot Sheet 22
P
T1
T2
LBP
Wa...
Transfer vs. Full Deep Architecture
5 Aug. 2014 Deep Learning - What's the buzz all about? / Debdoot Sheet 23
Multi-scale
...
Endnote (almost there)
“It’s like in quantum physics at the beginning of the
20th century” Trishul Chilimbi (MSR, DNN, Ada...
Take home message
• Challenges
– Architectures
• Neural Nets vs. Others
– Implementation
• CPU vs. GPU vs. Cloud
– GPU (VL...
Upcoming SlideShare
Loading in …5
×

Deep Learning - What's the buzz all about

3,446 views

Published on

Deep learning is a genre of machine learning algorithms that attempt to solve tasks by learning abstraction in data following a stratified description paradigm using non-linear transformation architectures.

When put in simple terms, say you want to make the machine recognize some Mr. X with Mt. E in the background, then this task is a stratified or hierarchical recognition task. At the base of the recognition pyramid would be kernels which can discriminate flats, lines, curves, sharp angles, color; higher up will be kernels which use this information to discriminate body parts, trees, natural scenery, clouds, etc.; higher up will use this knowledge to recognize humans, animals, mountains, etc.; and higher up will learn to recognize Mr. X and Mt. E and finally the apex lexical synthesizer module would say that Mr. X is standing in front of Mt. E. Deep learning is all about how you make machines synthesize this hierarchical logic and also learn these representative kernels all by itself.

Deep learning has been extensively used to efficiently solve these kinds of problems from handwritten character recognition (NYU, U Toronto), speech recognition (Microsoft, Google Voice), lexical ordered speech synthesis (Google Voice, iPhone Siri), object and poster recognition (Cortica), image retrieval (Baidu), content filtering (Youtube, Metacafe, Twitter), product visibility tracking (GazeMetrix), computational medical imaging (IITKgp).

This talk will focus on the buzz around this topic and how firm does the buzz hold on to the claims it boasts of?

Published in: Technology
  • Be the first to comment

Deep Learning - What's the buzz all about

  1. 1. Deep Learning What’s the buzz all about? Dr. Debdoot Sheet Assistant Professor Department of Electrical Engineering Indian Institute of Technology Kharagpur www.facweb.iitkgp.ernet.in/~debdoot/
  2. 2. 5 Aug. 2014 2Deep Learning - What's the buzz all about? / Debdoot Sheet
  3. 3. 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 -Tom Mitchell 5 Aug. 2014 Deep Learning - What's the buzz all about? / Debdoot Sheet 3
  4. 4. Demystifying Learning 5 Aug. 2014 Deep Learning - What's the buzz all about? / Debdoot Sheet 4 Man 1 Man 2 Man 3Man 4 Great Wall logo Great Wall tower Kim Jung WangDebdoot Experience (E) Performance(P) Debdoot, Kim, Jung and Wang are standing near the Great Wall logo and the Great Wall tower is behind them.
  5. 5. How was it Learning? 5 Aug. 2014 Deep Learning - What's the buzz all about? / Debdoot Sheet 5 Man 1 Man 2 Man 3Man 4 Great Wall logo Great Wall tower Kim Jung WangDebdoot Salient Segments Objectify Detect humans Recognize inanimate Describe Scene Debdoot, Kim, Jung and Wang are standing near the Great Wall logo and the Great Wall tower is behind them. Recognize humans
  6. 6. Challenges 5 Aug. 2014 Deep Learning - What's the buzz all about? / Debdoot Sheet 6 Salient Segments Objectify Detect humans Recognize inanimate Describe Scene Recognize humans Salient Segments Objectify Detect humans Recognize inanimate Describe Scene Recognize humans Salient Segments Objectify Detect humans Recognize inanimate Describe Scene Recognize humans
  7. 7. Challenges 5 Aug. 2014 Deep Learning - What's the buzz all about? / Debdoot Sheet 7 Salient Segments Objectify Recognize inanimate Describe Scene Recognize humans LBP Wavelets HoG Body part recognition Human appearance Chroma clustering Posture realign Silhouette matching Recognize human Detect humans
  8. 8. Dilemma only in Machine Vision? • Speech Recognition and Signal Processing – Dahl, et al,(2012). Context dependent pre-trained deep neural networks for large vocabulary speech recognition. IEEE Trans. Audio, Speech, and Language Processing, 20(1), 33–42. • Handwriting Recognition – Bengio, et al (2006). Greedy layer-wise training of deep networks. In NIPS’2006. • Natural Language Processing – Hinton, (1986). Learning distributed representations of concepts. In Proc. 8th Conf. Cog. Sc. Society, pp. 1–12. • Hierarchical and Transfer Learning – Goodfellow, et al. (2011). Spike-and slab sparse coding for unsupervised feature discovery. In NIPS’2011 Workshop on Challenges in Learning Hierarchical Models. • Medical Imaging – Karri and Sheet, et al (2014). Deep learnt random forests for segmentation of retinal layers in optical coherence tomography images. In ISBI’2014. 5 Aug. 2014 Deep Learning - What's the buzz all about? / Debdoot Sheet 8
  9. 9. How to tackle this dilemma? 5 Aug. 2014 Deep Learning - What's the buzz all about? / Debdoot Sheet 9 Great Wall behind Great Wall logo beside Debdoot, Kim, Jung, Wang
  10. 10. Multilayer Perceptron (MLP) 5 Aug. 2014 Deep Learning - What's the buzz all about? / Debdoot Sheet 10 Hiddenlayers Hiddenlayers Hiddenlayers
  11. 11. MLP Learning, troubles thereof 5 Aug. 2014 Deep Learning - What's the buzz all about? / Debdoot Sheet 11 P T1 T2
  12. 12. MLP Learning troubles, why so? 5 Aug. 2014 Deep Learning - What's the buzz all about? / Debdoot Sheet 12 P T1 T2 LBP Wavelets HoG Body part recognition Human appearance
  13. 13. MLP Learning troubles, why so? 5 Aug. 2014 Deep Learning - What's the buzz all about? / Debdoot Sheet 13 P T1 T2 Chroma clustering Posture realign Silhouette matching Recognize human
  14. 14. MLP Learning troubles, why so? 5 Aug. 2014 Deep Learning - What's the buzz all about? / Debdoot Sheet 14 P T1 T2 ?
  15. 15. Deep Learning, origin and growth • Around 1950 – NN age – Neural Nets (McCulloch and Pitts, 1943) – Unsupervised Learn. (Hebb, 1949) – Supervised Learn. (Rosenblatt, 1958) – Associative Memory (Palm, 1980; Hopfield, 1982) • 1960 – Discovery of visual sensory cells that respond to Edges (Hubel and Wiesel, 1962) – Feed Forward Multi Layer Perceptron (FF-MLP) (Ivakhnenko, 1968) • 1980 – Neocognition – Convolution + WeightReplication + Subsampling (Fukushima, 1980) – Max Pooling – Back-propagation (Werbos, 1981; LeCunn, 1985, 1988) 5 Aug. 2014 Deep Learning - What's the buzz all about? / Debdoot Sheet 15
  16. 16. Deep Learning, origin and growth • 1980-2000 – Search for simple, low-complexity, problem-solvers – Recurrent Neural Network (RNN) (Hochreiter and Schmidhuber, 1996) – Local learning Feed forward NN (Dayan and Hinton, 1996) – Advanced gradient descent (Schaback and Werner, 1992) – Sequential Network Construction (Honavar and Uhr, 1988) – Unsupervised Pre-training (Ritter and Kohonen, 1989) – Auto-Encoder (Hinton et al., 1989) – Back Propagating Convolutional Neural Networks (LeCun et al., 1989, 1990a, 1998) 5 Aug. 2014 Deep Learning - What's the buzz all about? / Debdoot Sheet 16
  17. 17. Deep Learning, origin and growth • 2000 – Era of Deep Learning – NIPS 2003 Feature Selection Challenge (Neal and Zhang, 2006) – MNIST digit recognition (LeCun et al., 1989) – Deep Belief Network (DBN) / Restricted Boltzmann Machines (Hinton et al., 2006) – Auto Encoders (Bengio, 2009) • 2006 – GPU based CNN (Chellapilla et al., 2006) • 2009 – GPU DBN (Raina et al., 2009) • 2011 – Max-Pooling CNN on the GPU (Ciresan et al., 2011) • 2012 – Image Net (Krizhevsky et al., 2012) 5 Aug. 2014 Deep Learning - What's the buzz all about? / Debdoot Sheet 17
  18. 18. Science fiction or reality Synthetic facial expression Machine that can dream 5 Aug. 2014 Deep Learning - What's the buzz all about? / Debdoot Sheet 18 Susskind, Joshua M., et al. "Generating facial expressions with deep belief nets." Affective Computing, Emotion Modelling, Synthesis and Recognition (2008): 421-440.
  19. 19. Deep Learning, where • Facebook • Baidu – IDL • Cortica • Cortexica • Google – DNNresearch – Deep Mind • Microsoft – DNN – Adam project 5 Aug. 2014 Deep Learning - What's the buzz all about? / Debdoot Sheet 19
  20. 20. COMPUTATIONAL MEDICAL IMAGING my experiences with Deep Learning (or not) for 5 Aug. 2014 Deep Learning - What's the buzz all about? / Debdoot Sheet 20     xx |,,|11 nnff     x,,,| 1 ng  
  21. 21. (Not so Deep) Transfer Learning 5 Aug. 2014 Deep Learning - What's the buzz all about? / Debdoot Sheet 21 D. Sheet, et al, Iterative self- organizing atherosclerotic tissue labeling in intravascular ultrasound images and comparison with virtual histology, IEEE TBME, 59(11), 2012 D. Sheet, et al, Hunting for necrosis in the shadows of intravascular ultrasound, CMIG, 38(2), 2014 D. Sheet, et al, Joint learning of ultrasonic backscattering statistical physics and signal confidence primal for characterizing atherosclerotic plaques using intravascular ultrasound, Med. Image Anal,18(1), 2014 Training set of IVUS signals Ground truth Random forest learning Test IVUS signal Labeled tissue Multi-scale Speckle stats. Multi-scale Speckle stats.
  22. 22. Local Minima Juggling, experience 5 Aug. 2014 Deep Learning - What's the buzz all about? / Debdoot Sheet 22 P T1 T2 LBP Wavelets HoG Body part recognition Human appearance Chroma clustering Posture realign Silhouette matching Recognize human
  23. 23. Transfer vs. Full Deep Architecture 5 Aug. 2014 Deep Learning - What's the buzz all about? / Debdoot Sheet 23 Multi-scale modeling of OCT speckles Training image set Ground truth Random forest learning Multi-scale modeling of OCT speckles Test image Labeled tissue Stacked Auto- Encoders, Logistic Regression Random Forest Training image set Ground truth Convolution masks http://www.facweb.iitkgp.ernet.in/~debdoot/current.html
  24. 24. Endnote (almost there) “It’s like in quantum physics at the beginning of the 20th century” Trishul Chilimbi (MSR, DNN, Adam) “The experimentalists and practitioners were ahead of the theoreticians. They couldn’t explain the results. We appear to be at a similar stage with DNNs. We’re realizing the power and the capabilities, but we still don’t understand the fundamentals of exactly how they work.” 5 Aug. 2014 Deep Learning - What's the buzz all about? / Debdoot Sheet 24
  25. 25. Take home message • Challenges – Architectures • Neural Nets vs. Others – Implementation • CPU vs. GPU vs. Cloud – GPU (VLSI) architectures • Hierarchical Temporal Memory • Potential Causal Connection • Toolboxes – Theano (Python/SciPy) – Pylearn2 • More information – www.deeplearning.net – Schmidhuber (2014). Deep Learning in Neural Networks: An Overview (arXiv:1404.7828) – Bengio (2009). Learning Deep Architectures for AI. – Deng and Yu (2013). Deep Learning: Methods and Applications. • Conferences – Int. Conf. Learning Representations (ICLR) 5 Aug. 2014 Deep Learning - What's the buzz all about? / Debdoot Sheet 25

×