Machine Learning

Some ML content I’ve made, some of which I use for self reference.

Builds

  1. Semi-Supervised Learning Using Virtual Adversarial Training, this was an exploration in what is the furthest you can go on few labels.

  2. Modeling ML methods using regression for sin fitting and digit classification, RL for simulated pole balancing, and recurrent NN’s for language identification.

  3. Autocomplete from scratch.

  4. Feature detection using Harris key points, project summary here.

  5. Bonus: first ever CS project, stock prediction by parsing 10K’s using BeautifulSoup.

Notebooks

These are extensive Jupyter notebooks I’ve written for ML textbooks.

  1. Mathematics for Machine Learning: Python companion.

  2. Vectors, Matrices, Least Squares: Python and Julia companions.

Interactive Animations

  1. Building these helps me solidify concepts, this selection is ML related.

  2. Most are interactive in some way and likely don’t display well on mobile.

  3. Collapsing cells as you go eases rendering.

Math

Derivative

Limit

Min, Max

Integral

Bayes

Button press simulates finger position on catching a globe.

Taylor Series (interactive sliders)

Machine Learning Concepts

Linear Regression

Click.

Least Squares

Click and drag.

Perceptron

Click, drag and spacebar.

K-Means

Hackathons, Apps Craft