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Deep Learning
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Abhinav Tushar
September 10, 2015
Research
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Deep Learning
Introductory talk on deep learning
Abhinav Tushar
September 10, 2015
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Transcript
D E E P L E A R N I
N G
models AE / SAE RBM / DBN CNN RNN /
LSTM Memnet / NTM agenda questions What ? Why ? How ? Next ?
what why how next What ? AI technique for learning
multiple levels of abstractions directly from raw information
what why how next Primitive rule based AI Tailored systems
Hand Crafted Program Output Input
what why how next Classical machine learning Learning from custom
features Hand Crafted Features Learning System Output Input
what why how next Deep Learning based AI Learn everything
Learned Features (Lower Level) Learned Features (Higher Level) Learning System Output Input
None
https://www.youtube.com/watch?v=Q70ulPJW3Gk PPTX PDF (link to video below)
With the capacity to represent the world in signs and
symbols, comes the capacity to change it Elizabeth Kolbert (The Sixth Extinction) “
Why The buzz ?
what why how next Google Trends Deep Learning
what why how next
Crude timeline of Neural Networks 1950 1980 1990 2000 Perceptron
Backprop & Application NN Winter
2010 Stacking RBMs Deep Learning fuss
HUGE DATA Large Synoptic Survey Telescope (2022) 30 TB/night
HUGE CAPABILITIES GPGPU ~20x speedup Powerful Clusters
HUGE SUCCESS Speech, text understanding Robotics / Computer Vision Business
/ Big Data Artificial General Intelligence (AGI)
How its done ?
what why how next Shallow Network ℎ ℎ = (,
0) = ′(ℎ, 1) = (, ) minimize
what why how next Deep Network
what why how next Deep Network More abstract features Stellar
performance Vanishing Gradient Overfitting
what why how next Autoencoder ℎ Unsupervised Feature Learning
what why how next Stacked Autoencoder Y. Bengio et. all;
Greedy Layer-Wise Training of Deep Networks
what why how next Stacked Autoencoder 1. Unsupervised, layer by
layer pretraining 2. Supervised fine tuning
what why how next Deep Belief Network 2006 breakthrough Stacking
Restricted Boltzmann Machines (RBMs) Hinton, G. E., Osindero, S. and Teh, Y.; A fast learning algorithm for deep belief nets
Rethinking Computer Vision
what why how next Traditional Image Classification pipeline Feature Extraction
(SIFT, SURF etc.) Classifier (SVM, NN etc.)
what why how next Convolutional Neural Network Images taken from
deeplearning.net
what why how next Convolutional Neural Network
what why how next Convolutional Neural Network Images taken from
deeplearning.net
what why how next Convolutional Neural Network
what why how next The Starry Night Vincent van Gogh
Leon A. Gatys, Alexander S. Ecker and Matthias Bethge; A Neural Algorithm of Artistic Style
what why how next
what why how next Scene Description CNN + RNN Oriol
Vinyals et. all; Show and Tell: A Neural Image Caption Generator
Learning Sequences
what why how next Recurrent Neural Network Simple Elman Version
ℎ ℎ = ( , ℎ−1 , 0, 1) = ′(ℎ , 2)
what why how next Long Short Term Memory (LSTM) add
memory cells learn access mechanism Sepp Hochreiter and Jürgen Schmidhuber; Long short-term memory
None
what why how next
what why how next Fooling Deep Networks Anh Nguyen, Jason
Yosinski, Jeff Clune; Deep Neural Networks are Easily Fooled
Next Cool things to try
what why how next Hyperparameter optimization bayesian Optimization methods adadelta,
rmsprop . . . Regularization dropout, dither . . .
what why how next Attention & Memory NTMs, Memory Networks,
Stack RNNs . . . NLP Translation, description
what why how next Cognitive Hardware FPGA, GPU, Neuromorphic Chips
Scalable DL map-reduce, compute clusters
what why how next Deep Reinforcement Learning deepmindish things, deep
Q learning Energy models RBMs, DBNs . . .
https://www.reddit.com/r/MachineLearning/wiki
Theano (Python) | Torch (lua) | Caffe (C++) Github is
a friend
@AbhinavTushar ?