Neural Network Workshop: Difference between revisions

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=== Workshop Outline ===
=== Workshop Outline ===
*Installfest
**Python
***Install [http://numpy.scipy.org/ numpy] and [http://www.scipy.org/ scipy], OR install [http://www.sagemath.org/ Sage]
***Install [http://pybrain.org/docs/ PyBrain]
**R
***Install [http://www.r-project.org/ R]
***Install [http://cran.r-project.org/web/packages/nnet/index.html nnet]
*What is a neural net?
*What is a neural net?
**What does a real neural net in the brain look like?
**What does a real neural net in the brain look like?
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*How are neural nets trained?
*How are neural nets trained?
**Backpropagation
**Backpropagation
*Installfest
**Python
***Install [http://numpy.scipy.org/ numpy] and [http://www.scipy.org/ scipy], OR install [http://www.sagemath.org/ Sage]
***Install [http://pybrain.org/docs/ PyBrain]
**R
***Install [http://www.r-project.org/ R]
***Install [http://cran.r-project.org/web/packages/nnet/index.html nnet]
*Potential Workshop Projects
*Potential Workshop Projects
**Function Approximation: XOR
**Function Approximation: XOR

Revision as of 17:01, 25 January 2011

What: A hands-on workshop courtesy of the Machine Learning Group about Feedforward Neural Networks that includes some background 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 Outline

  • What is a neural net?
    • What does a real neural net 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 net do?
    • Universal Function Approximation
    • Classification: identifying classes of things
      • Binary classification vs. multi-class classification
    • Regression: predicting values from data
  • How are neural nets trained?
    • Backpropagation
  • Installfest
  • Potential Workshop Projects
    • Function Approximation: XOR
    • Multi-class Classification: Type of Iris
      • We'll be using this classic data set to predict the class of Iris based on measurements of the petals.
    • Binary Classification: Face or not a Face?
      • Preprocessing images using OpenCV
      • Here we'll implement a neural network that detects whether an image contains a human face, using PyBrain.
      • It might not do a good job...

Software

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