@Zen_with_AI: How exactly do neural networks "learn"? The core really boils down to two steps: ① Forward propagation: The input passe…
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The article explains the core mechanism of how neural networks learn, detailing forward propagation and backward propagation using the chain rule to compute gradients and adjust parameters.
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How exactly do neural networks “learn”?
The core really boils down to two steps:
① Forward propagation: The input passes through the computation nodes step by step, yielding the final result, while recording the intermediate computation processes along the way.
② Backward propagation: Starting from the result and working backward, use the chain rule to multiply the local derivatives of each node all the way back, ultimately obtaining the gradient of each parameter with respect to the Loss.
So, what we call “training a neural network” essentially is:
Compute once → See how much it errs → Backpropagate the error along the computation graph → Adjust the parameters → Compute again.
Countless simple multiplications, additions, and applications of the chain rule layered together form the core mechanism for training today’s large models.
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