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Presentation next week: cerebellum and supervised learning

Presentation next week: cerebellum and supervised learning

Presentation next week: cerebellum and supervised learning Kitazawa S, Kimura T, Yin PB. Cerebellar complex spikes encode both destinations and errors in arm movements. Nature. 1998 392(6675):494-7. Motor learning: learning algorithms - network and distributed representations

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676 views • 53 slides
CSE 473 Introduction to Artificial Intelligence Neural Networks

CSE 473 Introduction to Artificial Intelligence Neural Networks

CSE 473 Introduction to Artificial Intelligence Neural Networks. Henry Kautz Spring 2006. Training a Single Neuron. Idea: adjust weights to reduce sum of squared errors over training set Error = difference between actual and intended output Algorithm: gradient descent

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444 views • 29 slides
ANNs (Artificial Neural Networks)

ANNs (Artificial Neural Networks)

ANNs (Artificial Neural Networks). The perceptron. Perceptron. X is a (column) vector of inputs. W is a (column) vector of weights. ? or w 0 is the bias or threshold weight. Perceptron. Usage: training/learning l ocal vs. global minima supervised vs. unsupervised

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325 views • 28 slides
CHAPTER 2

CHAPTER 2

CHAPTER 2. Neuron Model and Network Architecture. Objectives. Introduce the simplified mathematical model of the neuron Explain how these artificial neurons can be interconnected to form a variety of network architectures Illustrate the basic operation of these neural networks.

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401 views • 21 slides
Machine Learning: Connectionist

Machine Learning: Connectionist

McCulloch-Pitts Neuron Perceptrons Multilayer Networks Support Vector Machines Feedback Networks Hopfield Networks. Machine Learning: Connectionist. Classification Pattern Recognition Memory Recall Prediction Optimization Noise Filtering. Uses. Input signals, x i Weights, w i

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348 views • 21 slides

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