Study-Notes on Nick Liu - Software Engineer

Finding the Bottom of a Valley Blindfolded: Understanding Gradient Descent

nickboy@users.noreply.github.com (Nick Liu) — Fri, 20 Feb 2026 00:00:00 +0000

Imagine you’re blindfolded on a mountain and you need to find the lowest valley. You can’t see anything, but you can feel the ground under your feet. What would you do? You’d feel which direction slopes downward, take a small step that way, and repeat. Congratulations — you just invented gradient descent, the algorithm behind nearly every modern AI system.

Why Should You Care?
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Optimization is everywhere. When your GPS finds the fastest route, when Netflix recommends a movie, when your phone recognizes your face — behind all of these is an algorithm trying to find the best possible answer from a sea of possibilities. Gradient descent is the workhorse algorithm that makes this happen.

How Machines Ask Smart Questions: Entropy & Information Gain

nickboy@users.noreply.github.com (Nick Liu) — Fri, 20 Feb 2026 00:00:00 +0000

Imagine you’re playing 20 Questions. You’re trying to guess what animal your friend is thinking of. Would you start with “Is it a golden retriever?” or “Does it live in water?” The second question is obviously smarter — it eliminates roughly half the possibilities in one shot. Decision trees in machine learning work exactly the same way, and they use entropy and information gain to figure out what the smartest question is.

What’s the Big Idea?
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When a machine learning algorithm builds a Decision Tree , it needs to decide which question to ask first. Should it split the data by color? By size? By temperature? The answer comes from a beautifully simple concept: ask the question that reduces uncertainty the most.

How Neural Networks Learn from Mistakes: Backpropagation Explained

nickboy@users.noreply.github.com (Nick Liu) — Fri, 20 Feb 2026 00:00:00 +0000

When a factory produces a defective product, how do you trace the problem back through the assembly line to find which worker made the mistake? Neural networks face the exact same challenge. They have layers of “workers” (neurons), and when the final output is wrong, they need to figure out who’s responsible — and by how much. The algorithm that solves this is called backpropagation, and it’s the reason deep learning works at all.

Neural Networks Are Everywhere
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Before we dive into how neural networks learn, let’s appreciate what they do. The phone in your pocket uses neural networks for face recognition, voice transcription, photo enhancement, and text prediction. Self-driving cars, medical image analysis, language translation — all neural networks.

Study-Notes on Nick Liu - Software Engineer

Finding the Bottom of a Valley Blindfolded: Understanding Gradient Descent

Why Should You Care? #

How Machines Ask Smart Questions: Entropy & Information Gain

What’s the Big Idea? #

How Neural Networks Learn from Mistakes: Backpropagation Explained

Neural Networks Are Everywhere #

Why Should You Care?
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What’s the Big Idea?
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Neural Networks Are Everywhere
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