Hinton's Theorem

Hinton's Theorem states that deep neural networks, which are complex computer models inspired by the brain, can be viewed as a series of transformations that progressively simplify data while preserving essential information. Essentially, each layer in the network extracts meaningful features and reduces noise, making it easier for the final layer to make accurate predictions or classifications. This theorem helps us understand why deep networks are powerful: they learn hierarchical representations, capturing simple patterns early on and combining them into more complex insights deeper inside the model.