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...

 Hello :) Today is Day 125! A quick summary of today: listened to day 2 lectures on causality and optimization of MLx Fundamentals learned a bit of STATA The schedule was: The recording of the lectures was released about 30 mins ago, so going over it will be my task for tomorrow.  The 1st lecture from Professor Kun Zhang from CMU was more specifically about causal representation learning.  For example finding hidden variables.  Here if we look at just the relationship between cholesterol and exercise (right graph) we can see they have a positive relationship. Which is quite weird, and when we incorporate age into the picture, we can see the actual negative relationship.  In this case there are treatment A and B for kidney stones, and if we just look at the overall, without accounting for stone size, we might conclude that B is better. But if we incorporate stone size into the picture, A is better in both cases. if we understand the problem well, there is ...

 Hello :) Today is Day 124! A quick summary of today: Introduction to Machine learning by  Volodymyr Kuleshov  from Cornell Univeristy  Naive Bayes practical session by Richard Willis from King's College London Factorization methods by  Cho-Jui Hsieh  from UCLA Introduction to Machine learning by Volodymyr Kuleshov from Cornell Univeristy  It was really an amazing introduction. Personally it did not cover new stuff for me, but as an intro I believe it was top. What is supervised ML, OLS, Covered Non-Linear Least Squares, Overfitting, Regularization Naive Bayes practical session by Richard Willis from King's College London The interesting bit was implementing a Naive Bayes classification model from scratch.  I feel like this is one of the simplest explanations of Bayes theorem that I have seen/read so far (of prior, likelohood and posterior). Richard Willis is a Phd Implementation Computing the prior Computing the likelihoods  Computing log p...

 Hello :) Today is Day 123! A quick summary of today: read about optimization algorithms, and maths for DL from Dive into Deep Learning Firstly, my notes from the optimization algorithms chapter Covered topics: Common challenges in DL optimization, convexity, convexity properties, SGD, momentum, Adagrad, RMSProp, Adadelta, Adam, LR schedulers As for the maths for DL This is actually in the apendix, but I decided to read through it, just to check if there is any interesting math.  The content is as follows: Having this appendix is very nice and definitely helps to answer questions like for example when I went to the roots of multicollinearity (on Day 121 ) - understanding the math helps understand the root of problems.  As for the MLx Fundamentals it started just as I am finishing this blog. The first day is from 9pm to 3am in my local time, so I will write about it in tomorrow's blog.  Schedule (time is in BST) That is all for today! See you tomorrow :)

 Hello :) Today is Day 122! A quick summary of today: discovered and read a bit of  Dive into Deep Learning I randomly found the book from some post on r/learnmachinelearning about books on DL (I always manage to find some new cool book from these weekly posts) It seems a bit crazy that such a book is available for free. It is adopted by so many university courses and so many great practitioners participated in its writing.  Every topic contains an executable colab linked to it, and its available in PyTorch, Tensorflow, MXNET and Jax.  Today I had a read through 3. Linear Neural Networks for Regression 4. Linear Neural Networks for Classification 5. Multilayer Perceptrons 6. Builders’ Guide The book is great because under each topic there is also a live discussion section where one can ask questions. Overall (so far!), I think  An Introduction to Statistical Learning  is better. It has code as well and very detailed exercises. The problem I encountered...