Machine Learning, Gradient Descent, Hessian Matrix, Optimization, Artificial Intelligence.
Step Downhill: We update the model’s parameters by moving a little bit in the direction opposite the gradient. The size of our step is controlled by the learning rate. Repeat: We keep doing this until we hopefully reach the valley floor. The loss function landscape our model explores isn’t always