loss and gradients

The Science Behind Deep Learning: How Machines Get Smarter

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Deep learning gets smarter by learning internal representations that reduce prediction error: data flows forward to produce an output, a loss measures the gap to the target, and gradients flow backward to update millions or billions of parameters so the model’s future guesses improve.​ Forward pass and loss Backpropagation and gradient descent Why depth helps … Read more

The Secret Language of AI: How Machines Actually Learn

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Machines learn by turning data into internal representations and then nudging those representations to make better predictions, using a feedback loop that compares guesses to reality and pushes parameters in the direction that reduces error.​ From data to predictions Backpropagation: the core update rule Intuition for gradient descent What deep nets actually “learn” Generalization vs. … Read more