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 | '''When''': Tenatively January 26, 2011 | ||
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. | ||
Where: In the back classroom | '''Where''': In the back classroom | ||
Who: Anyone who wants to participate, either come and learn or help teach. [https://www.noisebridge.net/mailman/listinfo/ml Join the mailing list!] | '''Who''': Anyone who wants to participate, either come and learn or help teach. [https://www.noisebridge.net/mailman/listinfo/ml Join the mailing list!] | ||
=== List of Topics === | === List of Topics === | ||
Revision as of 19:43, 23 December 2010
What: A hands-on workshop courtesy of the Machine Learning Group using Neural Networks that includes both the theory and practical implementation.
When: Tenatively January 26, 2011
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!
List of Topics
- Math Preliminaries
- Universal Approximators: Kolmogorov's Theorem
- Linear Algebra: vectors, matricies
- Optimization Theory: error functions, gradients
- Machine Learning: regression, classification
- Neural Networks
- Basic Architecture
- Activation Functions
- 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: