Collaborative Filtering, Matrix Factorization, Recommender Systems, Gradient Descent, Neural Networks.
self.optimizer.zero_grad() # Clear any previous epoch gradients loss.backward() # Calc gradient self.optimizer.step() # Update parameters # Create Model -> U and V MF_model = PytorchLinearModel(num_users, num_items, K, lr) # Training for curr_epoch in range(num_epochs): # Reconstruct R_hat from