PyTorch Basics | Part Ten | Loss Theory

When working with machine learning models, one of the key concepts to understand is loss. Loss is a measure of how well a model is performing, and it is used to update the model’s parameters during training. In this article, we will delve into the theory behind loss in PyTorch.

What is Loss?

In the context of machine learning, loss is a measure of how well a model is able to predict the target variable. The goal of training a machine learning model is to minimize the loss, which means that the model’s predictions are as close to the actual values as possible.

Types of Loss Functions

In PyTorch, there are various loss functions that can be used depending on the type of problem being solved. Some common loss functions include:

• Mean Squared Error (MSE)
• Cross Entropy Loss
• Binary Cross Entropy Loss
• Smooth L1 Loss

Calculating Loss in PyTorch

In PyTorch, the loss is calculated using the forward pass of the model and the target variable. The result is a value that represents how well the model is performing. This loss value is then used to update the model’s parameters using optimization algorithms such as stochastic gradient descent (SGD).

Conclusion

Understanding loss theory is crucial for building and training machine learning models in PyTorch. By using the right loss function and optimizing it effectively, it is possible to train models that are able to make accurate predictions on new data.