PPT-CS 478 – Backpropagation

1 Backpropagation CS 478 Backpropagation 2 CS 478 Backpropagation 3 CS 478 Backpropagation 4 CS 478 Backpropagation 5 Backpropagation Rumelhart early 80s Werbos 74 explosion of neural net interest. The weights on the connec tions between neurons mediate the passed values in both dire ctions The Backpropagation algorithm is used to learn the weights o f a multilayer neural network with a 64257xed architecture It performs gradient descent to try Bernard and D Johnston Division of Neuroscience Baylor College of Medicine Houston Texas 77030 Submitted 24 March 2003 accepted in 64257nal form 6 May 2003 Bernard C and D Johnston Distancedependent modi64257able threshold for action potential backp The backpropagation training algo rithm is explained Partial derivatives of the objective function with respect to the weight and threshold coefficients are de rived These derivatives are valuable for an adaptation process of the considered neural n 1. Unsupervised Learning and Clustering. In unsupervised learning you are given a data set with no output classifications. Clustering is an important type of unsupervised learning. PCA was another type of unsupervised learning. @.*;.@8;=1=1.=26.=1.B*;.27?.=27027Ԁ*7-=81*;.=1.2;478@5.-0.܀%1.8=1.;2698;=*7=2792;*=287,86./;86=1.9*=2.7=Ԁ478@270=1*=@.*;.=;.*=270*16*7+.270*7-=1*=@ Cost function. Machine Learning. Neural Network (Classification). Binary classification. . . 1 output unit. Layer 1. Layer 2. Layer 3. Layer 4. Multi-class classification . (K classes). K output units. 1. Nearest Neighbor Learning. Classify based on local similarity. Ranges from simple . nearest neighbor . to case-based and analogical reasoning. Use local information near the current query instance to decide the classification of that instance. Ashutosh. Pandey and . Shashank. . S. rikant. Layout of talk. Classification problem. Idea of gradient descent . Neural network architecture. Learning a function using neural network. Backpropagation algorithm. Introduction to Computer Vision. Basics of Neural Networks, and. Training Neural Nets I. Connelly Barnes. Overview. Simple neural networks. Perceptron. Feedforward. neural networks. Multilayer . perceptron and properties. 1. Learning Sets of Rules. CS 478 - Learning Rules. 2. Learning Rules. If (Color = Red) and (Shape = round) then Class is A. If (Color = Blue) and (Size = large) then Class is B. If (Shape = square) then Class is A. Deep Neural Networks . Huan Sun. Dept. of Computer Science, UCSB. March 12. th. , 2012. Major Area Examination. Committee. Prof. . Xifeng. . Yan. Prof. . Linda . Petzold. Prof. . Ambuj. Singh. Safety Training. purpose. Understand the appropriate safety measures and who to contact in an event of an emergency. Aid in the safety of students in your designated building. Become familiar with the evacuation and assembly areas both inside and outside of your building. Yann . LeCun, Leon Bottou, . Yoshua Bengio and Patrick Haffner. 1998. . 1. Ofir. . Liba. Michael . Kotlyar. Deep learning seminar 2016/7. Outline. Introduction . Convolution neural network -. LeNet5. for the Mass Markets. Alex Polozov. polozov@cs.washington.edu. Microsoft PROSE team. prose-contact@microsoft.com. Jan 20, 2017. 1. UC Berkeley. https://microsoft.github.io/prose. PRO. gram. . S. ynthesis using .