Neural networks aren’t magic — they’re trillion‑parameter math engines pretending to think. 🧠⚡✨ Each neuron takes numbers in, multiplies by learned weights, adds a bias ➕, passes through a nonlinearity 🔄, and pushes activations forward ⚡. Stacked across thousands of layers and trillions of parameters, this becomes a gigantic function approximator that models language, images, code, and reasoning as high‑dimensional patterns 🗣️🖼️📜. During training, gradient descent 📉 and backpropagation 🔄
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