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Statistical Physics Methods in Machine Learning
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Classroom Contents
Deep Learning Applications by Rina Panigrahy
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- 1 Statistical Physics Methods in Machine Learning
- 2 Deep Learning Applications
- 3 Tutorial: Deep Learning
- 4 Outline
- 5 Learning an unknown function
- 6 Learning an unknown function: like curve fitting
- 7 Learning a function: why?
- 8 Learning a function: How
- 9 Linear Regression: Line fitting
- 10 Minimize errorloss in prediction
- 11 Loss measures error in prediction
- 12 Gradient descent
- 13 Learning a function: Linear Regression x
- 14 Gradient update: BackPropagation.
- 15 Stochastic Gradient Descent: gradients over a few examples at a time.
- 16 Learning a function: Sigmoid, sign
- 17 Sigmoid, RELU
- 18 Logistic regression uses logloss
- 19 Deep Network. Allows rich representation Can express any function/circuit
- 20 Neurons
- 21 Network of Neurons
- 22 Hierarchical representation of Objects
- 23 Training w: SGD to Minimize loss
- 24 Backpropagation: Gradient Descent for one example
- 25 Softmax for multiclass output: just like max
- 26 Convergence of Gradient Descent for Model training
- 27 Applications
- 28 MNIST
- 29 Convolution and Pooling
- 30 Gradient-Based Learning Applied to Document Recognition
- 31 Goal
- 32 ImageNet
- 33 ILSVRC
- 34 Architecture
- 35 RELU Nonlinearity
- 36 96 Convolutional Kernels
- 37 Phone recognition on the TIMIT benchmark Mohamed, Dahl, & Hinton,
- 38 Word error rates from MSR, IBM, & Google Hinton et. al. IEEE signal Processing Magazine, Nov 2012
- 39 Speech recognition
- 40 RNN
- 41 Videos/tutorials on Deep learning applications
- 42 Theoretical Understanding? - Deep Learning
- 43 Nonconvex Optimization
- 44 Low rank Approximation
- 45 No local minima in linear networks [Kawaguchi, NIPS 16, Ge et al, ICML 17]
- 46 Deep Learning
- 47 Does well experimentally
- 48 With simplifications, our target functions f are...
- 49 Overview of Results
- 50 Q&A
