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=== 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 '''Please try and download whatever you can BEFORE coming to Noisebridge!!!''' **Python ***Install [http://numpy.scipy.org/ numpy] and [http://www.scipy.org/ scipy] OR install [http://www.sagemath.org/ Sage] OR [http://www.virtualbox.org/ VirtualBox]+Joe's Python Scientific ***Code at http://www.mindmech.com/nnw.tgz **Computing Disk Image ***Install [http://pybrain.org/docs/ PyBrain] **R ***Install [http://www.r-project.org/ R] ***Code Repository: git clone git://ml-noisebridge.git.sourceforge.net/gitroot/ml-noisebridge/ml-noisebridge *Potential Workshop Projects **Function Approximation: XOR **Multi-class Classification: Type of Iris ***We'll be using this [http://archive.ics.uci.edu/ml/datasets/Iris 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 [http://opencv.willowgarage.com/wiki/ OpenCV] ***Here we'll implement a neural network that detects whether an image contains a human face, using [http://pybrain.org PyBrain]. ***It might not do a good job...
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