50+ days of Machine Learning

 Hello :) Today is Day 53! A quick summary of today Started Stanford University's cs231n , and today I covered: Image Classification: Data-driven Approach, k-Nearest Neighbor, train/val/test splits Linear classification: Support Vector Machine, Softmax Optimization: Stochastic Gradient Descent Backpropagation, Intuitions First I will quickly share my notes from the 1st three, and then for the big one (backprop) - will do it last. 1st lecture - Image classification using KNN Actually my impression is not so much from the theory in the lecture, but from the assignment exercises which the course has.  The KNN assignment was about implementing the loss of KNN (L1/L2 distance) and how to do it efficiently.  Firstly there was the most inefficient one - with two for loops Then it was with one for loop And then, the most efficient one which uses matrix multiplication magic I have never implemented knn by myself so this was a nice challenge. Also all 3 were compared and the result...

 Hello :) Today is Day 52! A quick summary of today: GPT from scratch with Andrej Karpathy Re-watching his other tutorials  Firstly, I will mention about what I learned from the GPT from scratch tutorial. Andrej Karpathy's videos introduced me to PyTorch for the 1st time. Actually the first video I saw which was about building a neural network from scratch, I kinda got what was happening, but as he went deeper and deeper and started writing code as if it was PyTorch (or building a neural network on a lower level compared to TensorFlow), I felt like I got slapped in the face hahaha. TF is much higher level than PyTorch, and at first it felt weird to have to write the whole dataset creation, model, training, eval, etc by myself. But with some practice it grew on me and now PyTorch feels more comfortable than TF, it feels more 'down-to-earth' ('down-to-python') haha.  So, on Day 46 I really tried to understand and learn transformers through KAIST's professor Choi a...

 Hello! Today is Day 51 :) A quick summary of today: Lecture 8: High-dimensional Space  Lecture 9: Random Walks and Markov Chains  Lecture 10: VC-Dimension  Just like previous days, I uploaded them to my google drive , and below are the pics themself.  I will try to do some of the questions in the next few days. High dim space Random walks and Markov chains VC-dimension That is all for today! See you tomorrow :)