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Lecture 5: Neural Language Models

Lecture 5: Neural Language Models

Lecture 5: Neural Language Models. CSCI 544: Applied Natural Language Processing Nanyun (Violet) Peng. Recap: Smoothing as Optimization -- Conditional Modeling. Given a context x Which outcomes y are likely in that context? We need a conditional distribution p(y | x)

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454 views • 41 slides
The Appeal of Parallel Distributed Processing

The Appeal of Parallel Distributed Processing

The Appeal of Parallel Distributed Processing. J.L. McClelland, D.E. Rumelhart, and G.E. Hinton ???? ???? ???. Contents. 1. Introduction 2. Parallel Distributed Processing 3. Examples of PDP Models 4. Representation and Learning In PDP Models 5. Origins of Parallel Distributed Processing.

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500 views • 29 slides
Learning Networks and Connective Knowledge

Learning Networks and Connective Knowledge

Learning Networks and Connective Knowledge. Stephen Downes October 16, 2006. Cognitivism. The idea that there is something that is, say, a belief And that this belief represents something, is about something And the belief takes on a concrete form , eg., sentences in the brain.

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276 views • 26 slides
CSC321: 2011 Introduction to Neural Networks and Machine Learning Lecture 5: Distributed Representations

CSC321: 2011 Introduction to Neural Networks and Machine Learning Lecture 5: Distributed Representations

CSC321: 2011 Introduction to Neural Networks and Machine Learning Lecture 5: Distributed Representations. Geoffrey Hinton. Localist representations. The simplest way to represent things with neural networks is to dedicate one neuron to each thing. Easy to understand. Easy to code by hand

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445 views • 24 slides
CSC2535: Computation in Neural Networks Lecture 12: Representing things with neurons

CSC2535: Computation in Neural Networks Lecture 12: Representing things with neurons

CSC2535: Computation in Neural Networks Lecture 12: Representing things with neurons. Geoffrey Hinton. Localist representations. The simplest way to represent things with neural networks is to dedicate one neuron to each thing. Easy to understand. Easy to code by hand

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241 views • 23 slides

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