👉 At its core, the math behind machine learning, particularly neural networks, relies on optimizing a function to minimize error between predicted outputs and actual targets. This optimization is achieved through gradient descent, where the algorithm iteratively adjusts the network's parameters (weights and biases) in the direction that reduces this error. The process involves calculating the gradient of the loss function with respect to each parameter, which indicates how much the error changes when the parameters are adjusted by a small amount. Using this gradient, the network updates its parameters in the opposite direction of the gradient, effectively "descending" the error landscape. This mathematical framework allows neural networks to learn complex patterns in data by adjusting their internal parameters, making them powerful tools for tasks like image recognition, natural language processing, and more.
