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CSC321: Neural Networks PowerPoint PPT Presentation
CSC321: Neural Networks - CSC321: Neural Networks Geoffrey Hinton * Title: lec3 Author: hinton Last modified by: hinton Created Date: 9/28/2002 3:36:33 AM Document presentation format:
CSC321: Neural Networks Geoffrey Hinton * Title: lec3 Author: hinton Last modified by: hinton Created Date: 9/28/2002 3:36:33 AM Document presentation format:
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CSC321: Programming Languages PowerPoint PPT Presentation
CSC321: Programming Languages - Collection of reserved words or keywords. Case sensitivity C-like: yes Early languages: no PHP: partly yes, ... Programming Languages Chapter 4: ...
Collection of reserved words or keywords. Case sensitivity C-like: yes Early languages: no PHP: partly yes, ... Programming Languages Chapter 4: ...
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CSC321 Lecture 25: More on deep autoencoders PowerPoint PPT Presentation
CSC321 Lecture 25: More on deep autoencoders - Use an RBM for the interactions between hidden and visible variables. ... joints and then using lots of infrared cameras to track the 3-D positions of the ...
Use an RBM for the interactions between hidden and visible variables. ... joints and then using lots of infrared cameras to track the 3-D positions of the ...
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CSC321: Neural Networks Lecture 12: Clustering PowerPoint PPT Presentation
CSC321: Neural Networks Lecture 12: Clustering - Title: lec14 Author: hinton Last modified by: hinton Created Date: 9/28/2002 3:36:33 AM Document presentation format: On-screen Show Company: university of toronto
Title: lec14 Author: hinton Last modified by: hinton Created Date: 9/28/2002 3:36:33 AM Document presentation format: On-screen Show Company: university of toronto
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CSC321:%20Introduction%20to%20Neural%20Networks%20and%20Machine%20Learning%20Lecture%2019:%20Learning%20Restricted%20Boltzmann%20Machines PowerPoint PPT Presentation
CSC321:%20Introduction%20to%20Neural%20Networks%20and%20Machine%20Learning%20Lecture%2019:%20Learning%20Restricted%20Boltzmann%20Machines - Each word meaning is a point in semantic space and has its own basin of attraction. ... The right meaning can be recovered even if the bottom-up input is ...
Each word meaning is a point in semantic space and has its own basin of attraction. ... The right meaning can be recovered even if the bottom-up input is ...
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CSC321: Neural Networks Lecture 16: Hidden Markov Models PowerPoint PPT Presentation
CSC321: Neural Networks Lecture 16: Hidden Markov Models - CSC321: Neural Networks Lecture 16: Hidden Markov Models Geoffrey Hinton
CSC321: Neural Networks Lecture 16: Hidden Markov Models Geoffrey Hinton
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CSC321:%20Extra%20Lecture%20(not%20on%20the%20exam)%20Non-linear%20dimensionality%20reduction PowerPoint PPT Presentation
CSC321:%20Extra%20Lecture%20(not%20on%20the%20exam)%20Non-linear%20dimensionality%20reduction - Linear methods cannot interpolate properly between the leftmost and rightmost ... The method shown here does not interpolate properly either because it can only ...
Linear methods cannot interpolate properly between the leftmost and rightmost ... The method shown here does not interpolate properly either because it can only ...
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CSC321: Neural Networks Lecture 19: Simulating Brain Damage PowerPoint PPT Presentation
CSC321: Neural Networks Lecture 19: Simulating Brain Damage - CSC321: Neural Networks Lecture 19: Simulating Brain Damage Geoffrey Hinton
CSC321: Neural Networks Lecture 19: Simulating Brain Damage Geoffrey Hinton
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CSC321: Neural Networks Lecture 11: Learning in recurrent networks PowerPoint PPT Presentation
CSC321: Neural Networks Lecture 11: Learning in recurrent networks - CSC321: Neural Networks Lecture 11: Learning in recurrent networks Geoffrey Hinton
CSC321: Neural Networks Lecture 11: Learning in recurrent networks Geoffrey Hinton
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CSC321: Neural Networks Lecture 12: Learning without a teacher PowerPoint PPT Presentation
CSC321: Neural Networks Lecture 12: Learning without a teacher - CSC321: Neural Networks Lecture 12: Learning without a teacher Geoffrey Hinton
CSC321: Neural Networks Lecture 12: Learning without a teacher Geoffrey Hinton
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CSC321: Introduction to Neural Networks and Machine Learning Lecture 14: Clustering PowerPoint PPT Presentation
CSC321: Introduction to Neural Networks and Machine Learning Lecture 14: Clustering - CSC321: Introduction to Neural Networks and Machine Learning Lecture 14: Clustering Geoffrey Hinton
CSC321: Introduction to Neural Networks and Machine Learning Lecture 14: Clustering Geoffrey Hinton
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CSC321: Neural Networks Lecture 20 Learning Boltzmann Machines PowerPoint PPT Presentation
CSC321: Neural Networks Lecture 20 Learning Boltzmann Machines - Let the hidden units reach thermal equilibrium at a temperature of 1 (may use ... it only takes one step to reach thermal equilibrium when the visible units are ...
Let the hidden units reach thermal equilibrium at a temperature of 1 (may use ... it only takes one step to reach thermal equilibrium when the visible units are ...
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CSC321: Lecture 7:Ways to prevent overfitting PowerPoint PPT Presentation
CSC321: Lecture 7:Ways to prevent overfitting - Weight-decay involves adding an extra term to the cost function that penalizes ... Is there a way to determine the weight-decay coefficient automatically? ...
Weight-decay involves adding an extra term to the cost function that penalizes ... Is there a way to determine the weight-decay coefficient automatically? ...
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CSC321: Neural Networks Lecture 13: Learning without a teacher: Autoencoders and Principal Components Analysis PowerPoint PPT Presentation
CSC321: Neural Networks Lecture 13: Learning without a teacher: Autoencoders and Principal Components Analysis - CSC321: Neural Networks Lecture 13: Learning without a teacher: Autoencoders and Principal Components Analysis Geoffrey Hinton
CSC321: Neural Networks Lecture 13: Learning without a teacher: Autoencoders and Principal Components Analysis Geoffrey Hinton
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CSC321: Lecture 8: The Bayesian way to fit models PowerPoint PPT Presentation
CSC321: Lecture 8: The Bayesian way to fit models - If you use the full posterior over parameter settings, overfitting disappears! ... But it is not economical and it makes silly predictions. ...
If you use the full posterior over parameter settings, overfitting disappears! ... But it is not economical and it makes silly predictions. ...
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CSC321: Neural Networks Lecture 13: Clustering PowerPoint PPT Presentation
CSC321: Neural Networks Lecture 13: Clustering - How do we decide on the soft assignments? ... If we measure distances in centimeters instead of inches we get different soft assignments. ...
How do we decide on the soft assignments? ... If we measure distances in centimeters instead of inches we get different soft assignments. ...
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CSC321: Neural Networks Lecture 3: Perceptrons PowerPoint PPT Presentation
CSC321: Neural Networks Lecture 3: Perceptrons - The N-bit parity task : Requires N features of the form: Are ... Unlike parity, there are no simple summaries of the other pieces that tell us what will happen. ...
The N-bit parity task : Requires N features of the form: Are ... Unlike parity, there are no simple summaries of the other pieces that tell us what will happen. ...
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CSC321: Lecture 27: Support Vector Machines PowerPoint PPT Presentation
CSC321: Lecture 27: Support Vector Machines - ... to get a low error rate on unseen data. It would be really helpful if we could get a guarantee ... In the original input space we get a curved decision surface. ...
... to get a low error rate on unseen data. It would be really helpful if we could get a guarantee ... In the original input space we get a curved decision surface. ...
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CSC321 Lecture 18: Hopfield nets and simulated annealing PowerPoint PPT Presentation
CSC321 Lecture 18: Hopfield nets and simulated annealing - A Hopfield net is composed of binary threshold units with recurrent connections ... They can behave in many different ways: Settle to a stable state. Oscillate ...
A Hopfield net is composed of binary threshold units with recurrent connections ... They can behave in many different ways: Settle to a stable state. Oscillate ...
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CSC321: Neural Networks Lecture 2: Learning with linear neurons PowerPoint PPT Presentation
CSC321: Neural Networks Lecture 2: Learning with linear neurons - Your diet consists of fish, chips, and beer. You get several portions of each ... portions of fish. portions of chips. portions of beer. 50 50 50. 2 5 3 ...
Your diet consists of fish, chips, and beer. You get several portions of each ... portions of fish. portions of chips. portions of beer. 50 50 50. 2 5 3 ...
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CSC321: Neural Networks Lecture 20: Mixtures of Experts Revisited PowerPoint PPT Presentation
CSC321: Neural Networks Lecture 20: Mixtures of Experts Revisited - But how do we partition the dataset into subsets for each expert? ... This fraction is the posterior probability of expert i. What are vowels? ...
But how do we partition the dataset into subsets for each expert? ... This fraction is the posterior probability of expert i. What are vowels? ...
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CSC321: Introduction to Neural Networks and machine Learning Lecture 16: Hopfield nets and simulated annealing PowerPoint PPT Presentation
CSC321: Introduction to Neural Networks and machine Learning Lecture 16: Hopfield nets and simulated annealing - A first order Markov process for discrete symbols is defined by: ... Better: add 1 to top and number of symbols to bottom. ... of hidden nodes and symbols too ...
A first order Markov process for discrete symbols is defined by: ... Better: add 1 to top and number of symbols to bottom. ... of hidden nodes and symbols too ...
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CSC321: Neural Networks Lecture 6: Learning to model relationships and word sequences PowerPoint PPT Presentation
CSC321: Neural Networks Lecture 6: Learning to model relationships and word sequences - Margaret = Arthur Victoria = James Jennifer = Charles. Colin Charlotte ... (victoria has-brother arthur) (charlotte has-uncle arthur) this follows from the above ...
Margaret = Arthur Victoria = James Jennifer = Charles. Colin Charlotte ... (victoria has-brother arthur) (charlotte has-uncle arthur) this follows from the above ...
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CSC321 Introduction to Neural Networks and Machine Learning Lecture 3: Learning in multi-layer networks PowerPoint PPT Presentation
CSC321 Introduction to Neural Networks and Machine Learning Lecture 3: Learning in multi-layer networks - The 7-bit parity task 1011010 0 0111000 1 1010111 1. Why ... Unlike parity, there are no simple summaries of the other pieces that tell us what will happen. ...
The 7-bit parity task 1011010 0 0111000 1 1010111 1. Why ... Unlike parity, there are no simple summaries of the other pieces that tell us what will happen. ...
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CSC321: Neural Networks Lecture 10: Learning ensembles of Networks PowerPoint PPT Presentation
CSC321: Neural Networks Lecture 10: Learning ensembles of Networks - The predictors that are further than average from d make bigger than average squared errors. ... So how do we make the individual predictors disagree? ...
The predictors that are further than average from d make bigger than average squared errors. ... So how do we make the individual predictors disagree? ...
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CSC321 Lecture 27 Using Boltzmann machines to initialize backpropagation PowerPoint PPT Presentation
CSC321 Lecture 27 Using Boltzmann machines to initialize backpropagation - The non-linearity used for reconstructing bags of words ... Train on bags of 2000 words for 400,000 training cases of business documents. ...
The non-linearity used for reconstructing bags of words ... Train on bags of 2000 words for 400,000 training cases of business documents. ...
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CSC321 Lecture 24 Using Boltzmann machines to initialize backpropagation PowerPoint PPT Presentation
CSC321 Lecture 24 Using Boltzmann machines to initialize backpropagation - fish cheese vector count school query reduce bag pulpit iraq word. 0 0 2 2 0 ... Divide the counts in a bag of words vector by N, where N is the total number of ...
fish cheese vector count school query reduce bag pulpit iraq word. 0 0 2 2 0 ... Divide the counts in a bag of words vector by N, where N is the total number of ...
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CSC321: Neural Networks Lecture 24 Products of Experts PowerPoint PPT Presentation
CSC321: Neural Networks Lecture 24 Products of Experts - How to combine simple density models ... The combination is always vaguer than the individual distributions. ... Combining energy dimples ...
How to combine simple density models ... The combination is always vaguer than the individual distributions. ... Combining energy dimples ...
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CSC321: Computation in Neural Networks Lecture 21: Stochastic Hopfield nets and simulated annealing PowerPoint PPT Presentation
CSC321: Computation in Neural Networks Lecture 21: Stochastic Hopfield nets and simulated annealing - CSC321: Computation in Neural Networks Lecture 21: Stochastic Hopfield nets and simulated annealing Geoffrey Hinton
CSC321: Computation in Neural Networks Lecture 21: Stochastic Hopfield nets and simulated annealing Geoffrey Hinton
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Book Chapter 2 PowerPoint PPT Presentation
Book Chapter 2 - CSC321 6 Modelling Processes using FSP. 1. Section 6. Modelling Processes using FSP ... A countdown timer which beeps after N ticks, or can be stopped. ...
CSC321 6 Modelling Processes using FSP. 1. Section 6. Modelling Processes using FSP ... A countdown timer which beeps after N ticks, or can be stopped. ...
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Computer Architecture PowerPoint PPT Presentation
Computer Architecture - Augend. Addend. Sum. Carry. Half-Adder. CSC321. Combinational ... Input 1 is call the 'Augend' Input 2 is called the 'Addend' Input 3 is call the 'Carry-in' ...
Augend. Addend. Sum. Carry. Half-Adder. CSC321. Combinational ... Input 1 is call the 'Augend' Input 2 is called the 'Addend' Input 3 is call the 'Carry-in' ...
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If we have a big data set that needs a complicated model PowerPoint PPT Presentation
If we have a big data set that needs a complicated model - If we have a big data set that needs a complicated model, the full Bayesian ... random collection of one-dimensional datapoints (except for nasty special cases) ...
If we have a big data set that needs a complicated model, the full Bayesian ... random collection of one-dimensional datapoints (except for nasty special cases) ...
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INTRODUCTION TO ARTIFICIAL INTELLIGENCE PowerPoint PPT Presentation
INTRODUCTION TO ARTIFICIAL INTELLIGENCE - Rules of procedure may be strategies, heuristics or rules of thumb. 15. Some examples. Children do not like green bats. Big. Large vs small. Walk vs run. Catching ...
Rules of procedure may be strategies, heuristics or rules of thumb. 15. Some examples. Children do not like green bats. Big. Large vs small. Walk vs run. Catching ...
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CSC321: Neural Networks Lecture 14: Mixtures of Gaussians PowerPoint PPT Presentation
CSC321: Neural Networks Lecture 14: Mixtures of Gaussians - This makes it possible to judge different methods. ... Guaranteed to lie in the convex hull of the data. Could be big initial jump ...
This makes it possible to judge different methods. ... Guaranteed to lie in the convex hull of the data. Could be big initial jump ...
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