Gradient descent, how neural networks learn | DL2

Gradient descent is a key algorithm for training neural networks, enabling them to learn from data by minimizing a cost function. The process involves adjusting weights and biases based on the gradient of the cost function, allowing networks to classify images accurately, such as recognizing handwritten digits, despite not necessarily understanding the underlying patterns.