'Weight decay' presentation slideshows

Friday, 15 February 2008 William H. Hsu Department of Computing and Information Sciences, KSU http://www.kddresearch.org

Lecture 11 of 42. Artificial Neural Networks: Backprop (continued) and Intro to SVM. Friday, 15 February 2008 William H. Hsu Department of Computing and Information Sciences, KSU http://www.kddresearch.org/Courses/Spring-2008/CIS732/ Readings:

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Geoffrey Hinton

CSC2535: Lecture 3: Ways to make backpropagation generalize better, and ways to do without a supervision signal. Geoffrey Hinton. Overfitting. The training data contains information about the regularities in the mapping from input to output. But it also contains noise

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Chapter 4: Artificial Neural Networks

Chapter 4: Artificial Neural Networks. Artificial neural network(ANN) General, practical method for learning real-valued, discrete-valued, vector-valued functions from examples BACPROPAGATION ???? Use gradient descent to tune network parameters to best fit a training set of input-output pairs

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Lecture 2: Learning without Over-learning

Lecture 2: Learning without Over-learning. Isabelle Guyon isabelle@clopinet.com. Machine Learning. Learning machines include : Linear discriminant (including Naïve Bayes) Kernel methods Neural networks Decision trees Learning is tuning: Parameters (weights w or a , threshold b)

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Dental Data Mining: Practical Issues and Potential Pitfalls

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Lecture 2: Learning without Over-learning

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ALTERNATE LAYER SPARSITY & INTERMEDIATE FINE-TUNING FOR DEEP AUTOENCODERS

Submitted by: Supervised by: Ankit Bhutani Prof. Amitabha Mukerjee (Y9227094) Prof. K S Venkatesh. ALTERNATE LAYER SPARSITY & INTERMEDIATE FINE-TUNING FOR DEEP AUTOENCODERS. AUTOENCODERS. AUTO-ASSOCIATIVE NEURAL NETWORKS OUTPUT SIMILAR AS INPUT. DIMENSIONALITY REDUCTION.

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Clustering

Clustering. Hypothesis: Hebbian synaptic plasticity enables a perceptron to compute the mean of its preferred stimuli. Unsupervised learning. Sequence of data vectors Learn something about their structure Multivariate statistics Neural network algorithms Brain models.

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Lecture 1: Learning without Over-learning

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4. Associators and Synaptic Plasticity

4. Associators and Synaptic Plasticity. Fundamentals of Computational Neuroscience, T. P. Trappenberg, 2010. Lecture Notes on Brain and Computation Byoung-Tak Zhang Biointelligence Laboratory School of Computer Science and Engineering

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4. Associators and Synaptic Plasticity

4. Associators and Synaptic Plasticity. Fundamentals of Computational Neuroscience, T. P. Trappenberg, 2010. Lecture Notes on Brain and Computation Byoung-Tak Zhang Biointelligence Laboratory School of Computer Science and Engineering

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Geoffrey Hinton

CSC321: 2011 Introduction to Neural Networks and Machine Learning Lecture 9: Ways of speeding up the learning and preventing overfitting. Geoffrey Hinton. Five ways to speed up learning. Use an adaptive global learning rate Increase the rate slowly if its not diverging

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Tuesday, October 12, 1999 William H. Hsu Department of Computing and Information Sciences, KSU

Lecture 14. Midterm Review. Tuesday, October 12, 1999 William H. Hsu Department of Computing and Information Sciences, KSU http://www.cis.ksu.edu/~bhsu Readings: Chapters 1-7, Mitchell Chapters 14-15, 18, Russell and Norvig. Lecture 0: A Brief Overview of Machine Learning.

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Brain Damage: Algorithms for Network Pruning

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Lecture 4

Lecture 4. Neural Networks ICS 273A UC Irvine Instructor: Max Welling. Neurons. Neurons communicate by receiving signals on their dendrites. Adding these signals and firing off a new signal along the axon if the total input exceeds a threshold.

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