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] | '''What''': A hands-on workshop courtesy of the [https://www.noisebridge.net/index.php?title=Machine_Learning Machine Learning Group] about Feedforward Neural Networks that includes some background and practical implementation. | ||
'''When''': January 26, 2011 7:00pm - 10:00pm | '''When''': January 26, 2011 7:00pm - 10:00pm | ||
| Line 9: | Line 9: | ||
'''Who''': Anyone who wants to participate, either come and learn or help teach. [https://www.noisebridge.net/mailman/listinfo/ml 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... | '''Who''': Anyone who wants to participate, either come and learn or help teach. [https://www.noisebridge.net/mailman/listinfo/ml 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 | === Workshop Outline === | ||
*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 | **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 | ||
* | *Installfest '''Please try and download whatever you can BEFORE coming to Noisebridge!!!''' | ||
** | **Python | ||
***[http:// | ***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 | ||
***[http://pybrain.org PyBrain] | ***Install [http://pybrain.org/docs/ PyBrain] | ||
****[http:// | **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... | |||
=== | === Software === | ||
* | *[http://neuroph.sourceforge.net/ neuroph]: java | ||
*[http://leenissen.dk/fann/ FANN]: C++ | |||
*[http://www.heatonresearch.com/encog Encog]: java | |||
*[http://pybrain.org PyBrain]: python | |||
*[http://leenissen.dk/fann/ FANN] | *[http://cran.r-project.org/web/packages/nnet/index.html nnet] R | ||
*[http://www.heatonresearch.com/encog Encog] | |||
*[http:// | |||
*[http://cran.r-project.org/web/packages/nnet/index.html nnet] | |||
Latest revision as of 20:07, 26 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[edit | edit source]
- 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 numpy and scipy OR install Sage OR VirtualBox+Joe's Python Scientific
- Code at http://www.mindmech.com/nnw.tgz
- Computing Disk Image
- Install PyBrain
- R
- Install R
- Code Repository: git clone git://ml-noisebridge.git.sourceforge.net/gitroot/ml-noisebridge/ml-noisebridge
- Python
- 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?