data to patterns

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