What Are Neural Networks? (The Real Explanation)
Every major AI system — ChatGPT, image generators, voice assistants — runs on the same underlying structure: a neural network. Here's what that actually means, how training works, and what neural networks genuinely cannot do.
0:00 — Why neural networks power everything in AI
0:45 — What a node actually does
1:30 — Layers, depth, and how abstraction emerges
2:30 — Activation functions: why depth needs non-linearity
3:10 — How training works: weights, loss, and backpropagation
4:20 — Gradient descent explained simply
5:10 — How much training data is actually needed
5:50 — Parameters and scale
6:30 — Architecture differences: CNNs vs Transformers
7:10 — What neural networks are actually used for
7:40 — What they genuinely cannot do
8:10 — The mental model worth keeping
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