(Day 126) Optimization lecture by Chi Jin from Princeton University + using Docker for the 1st time

May 6, 2024

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

(Day 125) MLx Fundamentals Day 2 - Causal representation learning, optimization

May 5, 2024

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

(Day 124) MLx Fundamentals Day 1 - Intro to ML, Naive Bayes, Factorization methods

May 4, 2024

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

(Day 123] Optimization algorithms chapter from Dive into DL

May 3, 2024

Hello :) Today is Day 123!

A quick summary of today:

read about optimization algorithms, and maths for DL from Dive into Deep Learning

(Day 122) Dive into Deep Learning - Interactive deep learning book with code, math, and discussions

May 2, 2024

Hello :) Today is Day 122!

A quick summary of today:

discovered and read a bit of Dive into Deep Learning

(Day 121) Uncovering the full reason behing multicollinearity + Frequent itemset mining lecture

May 1, 2024

Hello :) Today is Day 121!

A quick summary of today:

finally uncovered the full story behind multicollinearity
covered lecture 2 from Stanford’s CS246: Mining massive datasets: Frequent itemset mining

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