Day 5 was intentionally lighter, but still meaningful.
I explored the intuition behind gradient descent how models iteratively adjust parameters to minimize error, and why step size and direction matter. I’m still forming a complete mental picture, but this felt like an important conceptual bridge from cost functions to actual optimization.
On the building side, I made a few small tweaks to my portfolio website, refining interactions and layout details. Nothing major, but enough to keep the project moving forward.
What I worked on today
ML: Learned the basics of gradient descent
Made minor portfolio updates
hmmmm....what did i do today?
Some days are about depth, others are about maintenance. Both count.
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