- Exercise 1. Introduction in Python
- Lecture 1. Introduction
- Exercise 2. Introduction in Python
- Exercise 3. Introduction into Python
- Lecture 2. Linear regression with one variable. Gradient descent
- Lecture 3. Operations with matrices and vectors - review
- Exercise 4. Linear regression with one variable. Gradient descent
- Exercise 4. Linear regression with one variable - data (data.zip)
- Exercise 5. Linear regression wih multiple variables. Feature scaling
- Exercise 5. Linear regression with multiple variables - script and data (regr_multiple.zip)
- Exercise 6.1. Logarithmic regression - script and data (log_regression.zip)
- Exercise 6.2. Regularization (log_regression_regularization.zip)
- Control data (data.zip)
- Lecture 4. Линейна регресия с множество променливи Linear regression with multiple variables
- Lecture 5. Практически проблеми при градиентно спускане. Нормализация. Скорост на обучение Practical problems in gradient descent. Normalization. Learning rate
- Lecture 6. Analytical calculation of parameters
- Lecture 7. Classification
- Lecture 8. Logistic regression
- Lecture 9. Regularization
- Exercise 7. Multiclass logarithmic regression. Presentation of neural network
- Lecture 10. Neural networks - representation
- Exercise 8. Neural network learning