Practical resources for machine learning for artists
This tutorial will introduce practical resources, applications, and code for getting started with machine learning for creative and artistic use. It will include an overview of the materials available with ml4a.github.io, as well as a survey of helpful classes, writings, and code made by others in the field.
The tutorial is loosely a practical follow-up on the more theoretical lecture "From principal components to puppyslugs" on Tuesday, but attendance to that is not required to take this session.
Tentative list of topics to cover
Data science tasks
- extracting features from images, sounds, text
- search query by feature vectors
- t-SNE and visualization
- training a neural network for image classification
- transfer learning for fine-tuning a neural net to custom image classes
- "neural net painter"
Real-time (mostly using openFrameworks)
- live image classification and object detection
- fast image search and substitution
- train classifier for images and sounds
- controlling music and visual art programs with Wekinator
- survey of tools used in making ml4a.github.io/demos
- convnet.js, keras.js
- resources for computer vision in the browser