Introduction to Neural Networks - Perceptron
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This is the first of part of the Portland Data Science introduction to neural networks, presented on Jan 26, 2016, at Simple, Inc.
Introduction to Neural Networks - Perceptron
- 1. Introduction to Neural Networks Part One: Perceptron Portland Data Science Group Hannes Hapke @hanneshapke 1/26/2016
- 2. Why Machine Learning? Why Neural Networks?
- 3. Algorithms that can learn from and make predictions on data. Source: https://en.wikipedia.org/wiki/Machine_learning
- 4. Source: https://github.com/jcjohnson/neural-style A Neural Algorithm of Artistic Style by Leon A. Gatys, Alexander S. Ecker, Matthias Bethge
- 5. def _and(a, b): if a: if a: return True return False Instead of writing very speci鍖c code
- 6. You can apply a handful of machine learning algorithms to
- 7. Recommend books, predict order volumes, predict 鍖ight delays, control your thermostat, drive a car, grade papers, detect fraud, identify animals, classify your emails,
- 8. How does a single neuron work? Source: https://en.wikipedia.org/wiki/Machine_learning
- 10. How can a single neuron be represented mathematically? Source: https://en.wikipedia.org/wiki/Machine_learning
- 12. How to activate the neuron?
- 13. Why is a bias w0 required?
- 15. How does the Perceptron learn?
- 16. 1. Initialize the weights with random values 2. For each training sample, compute the predicted y and update the weights accordingly
- 18. 0. Create a virtual environment with mkvirtualenv perceptron 1. Fork the iPython notebook https://github.com/hanneshapke/PDX-data-perceptron.git 2. Install the requirements pip install -r requirements.txt 3. Start the notebook with ipython notebook How to get started?
- 19. 1. Complete the net_input method 2. Complete the 鍖t method 2.1 Initialization of the Perceptron weights 2.2 Calculate the difference of w 2.3 Update the weights 3. Complete the predict method What to do?
- 20. What can you now predict?
- 21. AND operation Identify Irises (setosa vs. versicolor) Credit card fraud/approval XOR operation
- 22. What works well? What doesnt work well? Can you predict a reliable credit approval? Whats the deal with the XOR operation? Questions along the way
- 23. Thanks to you Thanks also to @hobsonlane @GrimmScientist @uglyboxer for their help to prepare this presentation @hanneshapke https://github.com/hanneshapke/PDX-data-perceptron.git Please submit pull requests to share your solution.