Optimizer, Gradient Descent, Machine Learning, Adam, Sgd.
global minima. All these points will have dy/dx=0 Now, if we consider x as the weights and y=f(x) as the loss function, dL/dW=0 Our equation of gradient descent until now, we have used, So, if dL/dW is 0, the weights remain the same and we get stuck in a sub-optimal state. These kinds of non-convex