50+ days of Machine Learning

 Hello! :) Today is Day 49 Quick summary of today: Continued with AI503 Continuous optimization - Chapter 7 When models meet data - Chapter 8 + 8.6 Model selection Linear regression - Chapter 9 I think I found a good method for studying these topics: 1) Read the chapter 2) Check the professor's slides to see which points He brough up 3) Take notes while reading the chapter again I am not sure I like how the photos look on the page (yesterday's blog) so I will share a google drive link folder of my notes.  But also upload them below. Continuous optimization When models meet data Linear regression That is all for today! See you tomorrow :)

 Hello :) Today is Day 48! Quick summary of today: Started Week 1 - Matrix decompositions of AI503 Math for AI Following the book, I read the chapter  and took notes on my tablet. I hope the pics are legible. But the content is: - Determinant and Trace - Eigenvalues and eigenvectors - Cholesky decomposition - Eigendecomposition - Singular Value Decomposition - Exercises Before the exerises it is mostly the theory. But the real deal was doing the exercises. In particular finding how to do SVD. Firstly I tried to learn how to calculate eigen values and eigenvectors, and then I moved onto SVD.  Finding the proper way to solve SVDs... it took me a while. Firstly I followed some medium post, but then it turned out that the person made mistakes which were pointed out in the comments. SVD formula is A = E*V*U. Some of the problem came from the fact that I was not sure the exact application of this, but I just wanted to learn how to do it now, because down the line I am sure it will lead to so