How deep networks quietly squeeze knowledge — and why extra layers help

Big neural networks don’t just memorize examples, they slowly turn messy input into simpler ideas. Most of the work, it turns out, is about compression — shrinking useless details while keeping what matters for prediction. Training first learns to fit the answers, then it drifts into a slow, noisy cleanup where representations get tighter, and this step starts after errors get small, it changes mode and becomes more random.

The random part, like gentle noise, actually helps the net find better, simpler ways to see patterns. When a layer finishes learning it sits near an efficient balance between simplicity and accuracy, so maps from input to a hidden step and then to the output become compact and clear. Adding more hidden layers makes this process much quicker — the job is split and the slow cleanup finishes faster, leading to faster training. The result: deeper models that learn smarter shortcuts, not just bigger memory, and they often generalize better than a single layer could.

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