# What are the different loss functions available in PyTorch?

Commonly used loss functions in PyTorch include:

- nn.CrossEntropyLoss is a commonly used loss function for multi-class classification problems.
- nn.MSELoss: The mean squared error loss function for regression problems.
- nn.BCELoss is a commonly used binary cross-entropy loss function in binary classification problems.
- nn.NLLLoss is a loss function that is commonly used with the LogSoftmax activation function.
- nn.KLDivLoss: Kullback-Leibler divergence loss function, used to measure the difference between two probability distributions.
- nn.BCEWithLogitsLoss combines the sigmoid activation function and binary cross-entropy loss function for binary classification problems.
- Smooth L1 loss function, typically used for regression problems.
- MarginRankingLoss: A ranking loss function used in sorting tasks.
- nn.MultiLabelSoftMarginLoss: Soft margin loss function for multi-label classification problems.
- nn.TripletMarginLoss is a loss function used to learn the distance between samples that are similar to each other.