Course curriculum

  • 1

    Course Videos

    • Presentation slides (63 pages)

    • Flucoma patches

    • 1. Introduction _ What is FluCoMa_

    • 2. Plan _ Outline

    • 3. Classification

    • 4. Multilayer-Perceptron

    • 5. A Musical Motivation for Classification

    • 6. Supervised vs. Unsupervised Learning

    • 7. Training a Classifier

    • 8. Feed-forward and Back-propagation

    • 9. Classification Patch

    • 10. The _error_ _Training fluid.mlpclassifier~

    • 11. Making Predictions with fluid.mlpclassifier

    • 12. Validation with Training & Testing Data

    • 13. Saving a Trained Neural Network for Later Use

    • 14. Doing Classification with fluid.mlpregressor~

    • 15. Artistic Use of Classification

    • 17. Automated Dataset Creation and Validation

    • 18. Neural Network Parameters (Object Attributes)

    • 19. Hiddenlayers

    • 20. Activation and Outputactivation

    • 21. Learnrate

    • 22. Maxiter

    • 23. Batchsize

    • 24. Validation

    • 25. Overfitting

    • 26. Momentum

    • 27. Q&A on Parameters

    • 28. Neural Network Regression with Audio Descriptors

    • 29. Musical Example

    • 30. Training fluid.mlpregressor~

    • 31. Wavetable Autoencoder

    • 32. @tapin and @tapout

    • 33. Final Q&A