Importance of Loss functions in Deep Learning and Python Implementation

We know in neural networks, neurons work with corresponding weight, bias and their respective activation functions. The weights get multiplied with the inputs and then activation function is applied to the element before going to the next layer. Finally, we get the predicted value (yhat) through the output layer. But prediction is always closer to the actual (y), which we term as errors. So, we define the loss/cost functions to capture the errors and try to optimize it though backpropagation.