Lecture 17: Neural Networks and Deep Learning. Instructor: Saravanan Thirumuruganathan
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1 Lecture 17: Neural Networks and Deep Learning Instructor: Saravanan Thirumuruganathan
2 Outline Perceptron Neural Networks Deep Learning Convolutional Neural Networks Recurrent Neural Networks Auto Encoders Neural Turing Machines Adversarial Inputs *Tricks, GPUs and Frameworks
3 Brains
4 Skynet
5 Perceptron
6 Perceptron
7 Perceptron
8 Perceptron
9 Perceptron
10 Perceptron
11 Perceptron: Limitations (XOR)
12 Network Architectures Three different architectures Single-layer feed-forward Multi-layer feed-forward Recurrent The architecture of a neural network is linked with the learning algorithm used to train.
13 Single-Layer Feed-Forward (FF) NN
14 Multilayer Artificial Neural Networks An artificial neural network (ANN) has a more complex structure than that of a perceptron model. The additional complexities may arise in a number of ways: The network may contain several intermediary layers between its input and output layers. The network may use types of activation functions other than the sign function.
15 Multilayer Perceptrons
16 Activation Functions
17 Multi Layer Perceptrons
18 Multi Layer Perceptrons Layers are typically fully connected Number of hidden units typically chosen by hand
19 ANN Learning To learn the weights of an ANN model, we need an efficient algorithm that converges to the right solution when a sufficient amount of training data is provided.
20 Gradient Descent
21 Gradient Descent
22 BackPropagation Backpropagation adjusts the weights of the NN in order to minimize the network total mean squared error.
23 BackPropagation Developed to address the problem of computing the error for the hidden nodes, since their output values are not known a priori. After the weights have been computed for each layer (activation), the weight update formula is applied in the reverse direction. Backpropagation allows us to use the errors for neurons at layer k + 1 to estimate the errors for neurons at layer k.
24 BackPropagation - Training Backpropagation consists of the repeated application of the following two passes. Forward pass: The network is activated on one example and the error of (each neuron of) the output layer is computed. Backward pass: The network error is used for updating the weights. The error is propagated backwards from the output layer through the network layer by layer. This is done by recursively computing the local gradient of each neuron.
25 BackPropagation - Termination Total mean squared error change: absolute rate of change in the average squared error per epoch is small enough Generalization based criterion: After each epoch, test NN for generalization performance If performance is acceptable, then stop
26 Neural Network Topology The number of layers and neurons depend on the specific task. In practice this issue is solved by trial and error. Two types of adaptive algorithms can be used: Begin with a large network and successively remove some neurons and links until network performance degrades. Begin with a small network and introduce new neurons until performance is satisfactory.
27 Neural Network Design Issues Data representation Network Topology Network Parameters
28 Deep Learning
29 Concept of depth Length of path from input to output Motivations for Deep Architectures Insufficient depth can hurt Depth 2 is enough in many cases price: required number of nodes in the graph (and number of parameters) grows very large The brain has a deep architecture Cognitive processes seem deep
30 Cognitive processes seem deep Humans organize their ideas and concepts hierarchically. Humans first learn simpler concepts and then compose them to represent more abstract ones. Engineers break-up solutions into multiple levels of abstraction and processing
31 Deep Learning Breakthroughs Training deep architectures is challenging Geoff Hinton, Yoshua Bengio, Yann LeCun (independently) published a series of papers in 2006/2007 that provided breakthroughs.
32 Deep Learning : Demos Demo Skype translate! Google image search DeepMind: Playing Atari Games
33 Slide References Stanford CS 231n Univ of Notre Dame: CSE 60647, Spring 2014 Bayesian Behavior Lab, UNW Princeton, COS598 Spring 2015: The Unreasonable Effectiveness of Big Visual Data Slides from Deep Learning tutorials Slides from DeepMind
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