50+ days of Machine Learning

 Hello :) Today is Day 126! A quick summary of today went over two of the lectures from Day 2 of MLx Fundamentals  - optimization by Chi Jin and practical session on optimization by Ziyan Wang from King's College London deployed a basic ML model using docker I wanted to go over Professor Jin's lecture again and take notes, because I plan on taking his lecture on optimization and reading some of the suggested books which I shared yesterday . Covered topics: Gradient descent, smooth functions, Descent Lemma, convex optimization, accelerated GD, stochastic optimization, SGD, nonconvex optimization. As for the practical tutorial, the material was covered in yesterday's post and nothing extra special was part of the live lecture.  Tomorrow I will rewatch and take notes on Professor Kun Zhang from CMU's lecture on causal representation learning. I also found a simple tutorial on deploying an ML model with docker. I registered for this course: DataTalksClub's MLops zoomca