Backpropagation Explained for Beginners (Part 1): Building the Intuition

Neural networks learn by systematically adjusting their internal weights and biases to minimize the error between their predictions and the actual outcomes. This training process involves finding the lowest point on a complex, high-dimensional loss surface, which represents the minimum possible error. To navigate this surface, a core algorithm called backpropagation uses the chain rule from calculus to determine how each individual parameter contributes to the overall error. These calculations reveal precisely how to update each weight and bias, allowing the network to incrementally improve its accuracy with each iteration.