We kicked issues off with a fairly relatable use case: predicting whether or not a T-shirt might be a prime vendor. The concept? Feed the community options like value, advertising and marketing finances, delivery value, and materials high quality. The output? A chance rating that claims, “Sure, this one’s going viral,” or “Nah, strive once more.”
However the place it actually received fascinating was shifting from good previous logistic regression to one thing extra versatile: neural networks.
Consider it like a stack of mini logistic regressions (a.ok.a. neurons) organized in layers. Each tries to study one thing helpful — like how reasonably priced a product is or how a lot consciousness it’s getting. Stack just a few of these layers collectively, and also you get what’s referred to as a multilayer perceptron (MLP).
Every layer builds extra advanced understanding. Like:
- Layer 1: edges or costs
- Layer 2: eyes or product consciousness
- Layer 3: faces or “ought to I purchase this?”
The cool half? Neural networks study options as an alternative of us manually defining them. That’s the magic sauce.
