Purpose of Loss Function

Loss Function and its Purpose

The loss function measures the difference between the algorithm's current output and its forecasted output. This is really a technique to assess what effectively your algorithm models the data.

Importance of the Loss Function

Peter Druker, a distinguished author, says What you can't determine, you can't evaluate. The loss function is useful to examine how well your algorithm models your dataset in this context.

If the loss function value is lower, the model is good; if not, we must improve the model's parameters to quality and lowering.

Comparing the loss and cost functions

Loss function and cost function are sometimes mucked up. Let's investigate the fundamentals of loss and cost functions.

Although they are close and generally referenced interchangeably, cost function and loss function are separate.

Loss Function:

There will only be one learning exemplar or input for a loss function or error function.

Cost Function:

On the other extreme, the average loss all throughout entire training dataset is a cost function.

With only a small selection of attack traces, deep learning can conquer targets covered by countermeasures, providing it an effective tool for side-channel analysis profiling.

However, hyperparameter modification is required, and it can be expensive to get strong attack capability.

Aside from the vast range of alternatives possible in the machine learning field, recent years have also marked the development of neural network sections built specifically for side-channel analysis.

The loss function, which computes the error or loss between the actual and desired output, is a significant hyperparameter. The deep learning neural network's weights for the connections between its neurons or filters are updated using the resulting loss. Unfortunately, there are no systematic comparisons between various loss functions, despite the fact that this hyperparameter is extremely important.

You can also check out loss function in deep learning .

Conclusion

In this blog , we explained about importance of loss function , and purpose of loss function.