# What are the commonly used loss functions in PyTorch?

Commonly used loss functions in PyTorch include:

- nn.MSELoss is a loss function used for regression tasks, measuring the mean squared error.
- nn.CrossEntropyLoss is a loss function commonly used for multi-class classification tasks.
- nn.NLLLoss is a loss function that is used for multi-classification tasks.
- nn.BCELoss: Binary Cross Entropy Loss function, used for binary classification tasks.
- nn.BCEWithLogitsLoss combines the Sigmoid activation function and binary cross-entropy loss function for binary classification tasks.
- CTCLoss: The CTCLoss, which stands for Connectionist Temporal Classification, is a loss function used in sequence labeling tasks.
- nn.KLDivLoss: KL Divergence loss function, used to measure the difference between two probability distributions.
- nn.SmoothL1Loss: A smooth L1 loss function used for regression tasks.
- nn.CosineEmbeddingLoss: A function for measuring the similarity between two vectors, using cosine embedding loss.
- nn.TripletMarginLoss is a loss function for learning feature representations by comparing triplets.