Day 8 Gradient Descent
Loss Functions — The Model’s Moral Compass
What if I told you almost every AI model is just trying to roll down a hill without slipping? ⚡
Welcome to Gradient Descent — the powerful algorithm behind how machines learn.
🔹 What is Gradient Descent?
Gradient Descent helps models find the lowest point on a loss curve — i.e., minimum error — by taking small, calculated steps downhill.
🧭 It’s like hiking down a foggy, bumpy trail:
The slope tells you which direction to step
Each step adjusts the model’s weights
The goal? Reach the valley of minimum loss
🔐 Security Lens: How Attackers Hijack the Learning Path
Gradient Descent may be smart — but attackers know how to poison the path. Here’s how:
⚠️ Misleading Road Signs (Gradient Manipulation)
📌 Data poisoning makes the gradient point in the wrong direction — training the model to learn wrong behaviors.
Think of fake road signs that send you off-course while hiking.
⚠️ Banana Peels on the Trail (Adversarial Examples)
📌 At test time, attackers add tiny changes to inputs that don’t look wrong — but cause major misclassifications.
Like stepping on an invisible banana peel — you slip, even though the path looks fine.
⚠️ Fake Shortcuts and Hidden Traps (Optimization Traps)
📌 Attackers craft malicious loss landscapes to trap the model in bad local minima.
You're tricked into thinking you’ve reached the valley — but it’s just a ditch.
📚 Key References
Biggio et al. (2012): Poisoning Attacks Against Support Vector Machines
Goodfellow et al. (2015): Explaining and Harnessing Adversarial Examples
Jovian: Gradient Descent Deep Dive
Aditya Bharathi: Security Perspectives
💬 Question for You
Have you battled frustrating optimization issues while training your models? I’d love to hear your story 👇
📅 Up Next: How Neural Networks become brainy "universal approximators" — and why that’s a double-edged sword 🧠⚡
🔗 Missed Day 7? Catch it here
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