Implementing different machine learning and optimization algorithms from scratch and visualizing them without any machine learning/optimization library.
- Perceptron
- Linear regression
- Logistic regression
- Polynomial regression
- Multiclass classification (One-vs-rest, One-vs-one)
- Nonlinear transformation
- Gradient descent
- Newton
- Line search (Bisection, Newton raphson, False position)
- Backtracking line search (Armijo, Goldstein, Weak-Wolfe, Strong-Wolfe)
- Frank-wolfe
- Feasible directions
- Projected gradient
- Reduced gradient