Deep Learning, Regularisation, Optimization Algorithms, Deep Neural Networks, Gradient Descent.
called an epoch A[L], Z[L] = forward_prop(X{t}, Y{t}) cost = compute_cost(A[L], Y{t}) grads = backward_prop(A[L], caches) update_parameters(grads) This is equivalent to 1 epoch (1 epoch = single pass through the training set). Note that the cost function for mini batch is given as: Where 1000 is