Neural Network Workshop: Difference between revisions

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'''What''': A hands-on workshop courtesy of the [https://www.noisebridge.net/index.php?title=Machine_Learning Machine Learning Group] using Neural Networks that includes both the theory and practical implementation.
'''What''': A hands-on workshop courtesy of the [https://www.noisebridge.net/index.php?title=Machine_Learning Machine Learning Group] using Neural Networks that includes both the theory and practical implementation.


'''When''': Tenatively January 26, 2011 7:00pm - 10:00pm
'''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''.
'''Why''': To raise Neural Network Awareness (NNA) and money for Noisebridge. ''Donations towards Noisebridge will be encouraged and appreciated''.

Revision as of 14:15, 3 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!. The math will be explained, but is not essential to totally understand in order to work with the examples given.

Workshop Overview, Resources

  • Math Preliminaries
  • Neural Networks
    • Basic Architecture
    • Activation Functions
      • Which activation functions are good? Hornik's 1991 Paper
    • Error Functions and Output Layers
      • Regression (univariate and multivariate)
      • Classification (binary and multi-class, logistic and softmax)
    • Training
      • Backpropagation
    • Implementation
      • Identifying Faces with Neural Nets
      • ??? Other such ideas

Software

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