Neural Network Workshop: Difference between revisions

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=== Workshop Overview, Resources ===
=== Workshop Overview, Resources ===
*Math Preliminaries
*What is a Neural Network?
**What does a real neural network in the brain look like?
**Differences between artificial neural nets and biological neural nets
**Basic architecture: layers of interconnected units
**Weights and activation functions: the actual computation
*What can Neural Networks do?
**Universal Function Approximators
**Universal Function Approximators
***[http://en.wikipedia.org/wiki/Universal_approximation_theorem Cybenko's Theorem]
**Classification: identifying classes of things
***[http://www.sztaki.hu/~csaji/CsBCs_MSc.pdf Some guy's master thesis]
***Binary classification vs. multi-class classification
**Linear Algebra: vectors, matricies
**Regression: predicting values from data
**Optimization Theory: error functions, gradients
*How are neural networks trained?
**Machine Learning: regression, classification
**Backpropagation
*Neural Networks
*Let's write some code! Face or not a Face? Binary Classification with Neural Nets
**Basic Architecture
**Here we'll implement a neural network that detects whether an image contains a human face, using the language of your choice:
**Activation Functions
***[http://neuroph.sourceforge.net/ neuroph]: java
***Which activation functions are good? Hornik's 1991 Paper
***[http://leenissen.dk/fann/ FANN]: C++
**Error Functions and Output Layers
***[http://www.heatonresearch.com/encog Encog]: java
***Regression (univariate and multivariate)
***[http://pybrain.org PyBrain]: python
***Classification (binary and multi-class, logistic and softmax)
****[http://cran.r-project.org/web/packages/nnet/index.html nnet] R
**Training
***Backpropagation
**Implementation
***Face or not a Face? Binary Classification with Neural Nets


=== Volunteers ===
=== Volunteers ===
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*Mike S
*Mike S


=== Software ===
=== Software ===
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*[http://torch5.sourceforge.net/manual/index.html Torch5]
*[http://torch5.sourceforge.net/manual/index.html Torch5]
*[http://pynn.gforge.inria.fr/ PyNN] (Biophysical Neural Nets)
*[http://pynn.gforge.inria.fr/ PyNN] (Biophysical Neural Nets)
*[http://cran.r-project.org/web/packages/nnet/index.html nnet]

Revision as of 01:25, 8 January 2011

What: A hands-on workshop courtesy of the Machine Learning Group using Neural Networks that includes both the theory and practical implementation.

When: January 26, 2011 7:00pm - 10:00pm

Why: To raise Neural Network Awareness (NNA) and money for Noisebridge. Donations towards Noisebridge will be encouraged and appreciated.

Where: In the back classroom

Who: Anyone who wants to participate, either come and learn or help teach. Join the mailing list!. It won't be too heavy on the math, so don't worry if you knowledge of vector spaces and derivatives is a bit of a null set...

Workshop Overview, Resources

  • What is a Neural Network?
    • What does a real neural network in the brain look like?
    • Differences between artificial neural nets and biological neural nets
    • Basic architecture: layers of interconnected units
    • Weights and activation functions: the actual computation
  • What can Neural Networks do?
    • Universal Function Approximators
    • Classification: identifying classes of things
      • Binary classification vs. multi-class classification
    • Regression: predicting values from data
  • How are neural networks trained?
    • Backpropagation
  • Let's write some code! Face or not a Face? Binary Classification with Neural Nets
    • Here we'll implement a neural network that detects whether an image contains a human face, using the language of your choice:

Volunteers

Put your name down here to volunteer and contact Mike S for coordination:

  • Mike S

Software

This is just a list of packages used for constructing and training neural networks: