Neural Network Workshop: Difference between revisions
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=== Workshop Overview, Resources === | === Workshop Overview, Resources === | ||
*What is a | *What is a neural net? | ||
**What does a real neural | **What does a real neural net in the brain look like? | ||
**Differences between artificial neural nets and biological neural nets | **Differences between artificial neural nets and biological neural nets | ||
**Basic architecture: layers of interconnected units | **Basic architecture: layers of interconnected units | ||
**Weights and activation functions: the actual computation | **Weights and activation functions: the actual computation | ||
*What can | *What can neural net do? | ||
**Universal Function Approximation | **Universal Function Approximation | ||
**Classification: identifying classes of things | **Classification: identifying classes of things | ||
***Binary classification vs. multi-class classification | ***Binary classification vs. multi-class classification | ||
**Regression: predicting values from data | **Regression: predicting values from data | ||
*How are neural | *How are neural nets trained? | ||
**Backpropagation | **Backpropagation | ||
*Face or not a Face? Binary | *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: | **Here we'll implement a neural network that detects whether an image contains a human face, using the language of your choice: | ||
***[http://neuroph.sourceforge.net/ neuroph]: java | ***[http://neuroph.sourceforge.net/ neuroph]: java | ||
Revision as of 01:32, 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 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
- Face or not a Face? Binary classification with neural nets.
Software
This is just a list of packages used for constructing and training neural networks: