A computational technique in mathematics and machine learning where a program automatically calculates the derivatives (rates of change) of complex functions.
Combines auto- (self, automatic) + differentiation (the mathematical process of finding derivatives). Emerged as a formal computing concept in the 1960s-70s, essential for modern machine learning.
Autodifferentiation is the hidden mathematical superhero behind almost every AI and deep learning system—without it, training neural networks would be computationally impossible, yet most people using AI have never heard the term.
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