👉 The math underlying a neural network involves linear algebra, calculus, and probability theory. Linear algebra is used to represent data as vectors and perform operations like matrix multiplications, which are essential for transforming inputs into meaningful features. Calculus, particularly gradient descent, is employed to optimize the network's weights by minimizing a loss function, ensuring that predictions become more accurate over time. Probability theory helps model uncertainties and make predictions based on data distributions, often through techniques like Bayesian inference. Together, these mathematical foundations enable neural networks to learn complex patterns and make predictions on new data.
