These are some machine learning notes, thoughts, tips & tricks that I’ve collected throughout the years of my studies. Some of them are from Introduction to Machine Learning and Neural Networks and Deep Learning taken at UCLA. Others are from various online courses such as Stanford’s CS231n, or books such as the deep learning book.
Please send me a pull request if you find any mistakes, inaccuracies, or points that could be clarified better!
Linear and Logistic Regression
- Probabalistic Interpretation for Linear Regression & Gradient Descent
- Linear & Logistic Regression, Convexity, Hessian Matrices, and closed-form solutions
- Regularized (ridge) linear regression