My ML Journey

Why I'm Documenting This

Learning ML in public. Every entry is a day of studying: what clicked, what confused me, and what I'm pulling on next. Writing forces me to actually understand things, not just watch them go by. If it helps someone else figure something out, even better.

Daily notes on machine learning, deep learning, and AI.

8 entries

July 9, 2026·2 min read

Backpropagation Through Time Is Just Backprop That Remembers Every Timestep

An RNN's hidden state at time $t$ depends on the hidden state at $t-1$, which depends on $t-2$, all the way back to the start of the sequence. That chain is wha

July 4, 2026·2 min read

Gradient Descent Doesn't Know Where the Minimum Is: It Only Knows Which Way Is Down

Backpropagation tells you the gradient: how much each weight should change to reduce the cost. Gradient Descent is the algorithm that actually uses that gradien

July 3, 2026·2 min read

Backpropagation Isn't Magic: It's the Chain Rule Run Backwards

A network with random weights makes garbage predictions on the first pass. The entire question that makes deep learning work is: given how wrong an output was,

June 29, 2026·2 min read

Feature Selection Picks What to Keep, Feature Extraction Builds Something New

Not all features in a dataset are worth keeping. Some are redundant. Some are noise. Some slow down training without adding any predictive signal. Feature engin

June 28, 2026·2 min read

Ridge Shrinks Coefficients, Lasso Zeros Them Out: Why That Difference Matters

Regularization is not about making your model more accurate on training data. It is about making it less wrong on data it has never seen. When a model fits too

June 26, 2026·1 min read

MSE Penalizes Big Mistakes Harder: R² Tells You If Your Model Even Learned Anything

You've trained a regression model. It outputs numbers. How do you know if those numbers are any good? Two metrics cover this from different angles: MSE measures

June 19, 2026·1 min read

Logistic Regression Isn't Regression: It's Classification Through a Probability Trick

The name is confusing. Logistic Regression sounds like it predicts continuous values the way Linear Regression does, but it's actually a classification algorith

June 18, 2026·1 min read

Linear Regression Is Just Finding the Best Straight Line: Here's What That Actually Means

Linear regression is the first algorithm most people learn and often the most underestimated. At its core, it does one thing: fits a straight line through data