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Chapter 6: Backpropagation Nets

Chapter 6: Backpropagation Nets

Chapter 6: Backpropagation Nets. Architecture : at least one layer of non-linear hidden units Learning : supervised, error driven, generalized delta rule Derivation of the weight update formula (with gradient descent approach) Practical considerations Variations of BP nets Applications.

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392 views • 39 slides
Ch 5. Neural Networks (1/2) Pattern Recognition and Machine Learning, C. M. Bishop, 2006.

Ch 5. Neural Networks (1/2) Pattern Recognition and Machine Learning, C. M. Bishop, 2006.

Ch 5. Neural Networks (1/2) Pattern Recognition and Machine Learning, C. M. Bishop, 2006. Summarized by M.-O. Heo Biointelligence Laboratory, Seoul National University http://bi.snu.ac.kr/. Contents. 5.1 Feed-forward Network Functions 5.1.1 Weight-space symmetries 5.2 Network Training

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493 views • 25 slides
Principles of Back-Propagation Prof. Bart ter Haar Romeny

Principles of Back-Propagation Prof. Bart ter Haar Romeny

The relation between biological vision and computer vision. Principles of Back-Propagation Prof. Bart ter Haar Romeny. How does this actually work?. Deep Learning Convolutional Neural Networks. In. AlexNet (Alex Krizhevsky 2012). Error backpropagation. ImageNet challenge: 1.4 million

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252 views • 22 slides
Artificial Neural Networks (ANNs) and the Error Backpropagation Procedure

Artificial Neural Networks (ANNs) and the Error Backpropagation Procedure

Artificial Neural Networks (ANNs) and the Error Backpropagation Procedure. Prof. Carolina Ruiz Department of Computer Science Worcester Polytechnic Institute. A 2-layer feedforward ANN. Input hidden layer output layer. -1. -1. -1. -1. -1. -1.

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84 views • 7 slides

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