A short overview over the !PyTorch operations most relevant for this course. The full documentation can be accessed [[https://pytorch.org/docs/stable/index.html|here]].

|| '''Operation''' || '''Description''' ||
|| [[https://pytorch.org/docs/stable/generated/torch.empty.html|torch.empty]] || Create a tensor of a specific shape with uninitialized data ||
|| [[https://pytorch.org/docs/stable/generated/torch.zeros.html|torch.zeros]] || Create a tensor of a specific shape and fill it with zeros ||
|| [[https://pytorch.org/docs/stable/generated/torch.ones.html|torch.ones]] || Create a tensor of a specific shape and fill it with ones ||
|| [[https://pytorch.org/docs/stable/generated/torch.tensor.html|torch.tensor]] || Create a tensor with the given data ||
|| [[https://pytorch.org/docs/stable/generated/torch.sparse_coo_tensor.html|torch.sparse_coo_tensor]] || Create a sparse tensor with the given indices and values ||
|| [[https://pytorch.org/docs/stable/generated/torch.matmul.html|torch.matmul]] || Perform matrix multiplication ||
|| [[https://pytorch.org/docs/stable/generated/torch.nonzero.html|torch.nonzero]] || Find the indices where a tensor contains non-zero values ||
|| [[https://pytorch.org/docs/stable/generated/torch.sort.html|torch.sort]] || Return the sorted values and sorting permutation of a tensor ||
|| [[https://pytorch.org/docs/stable/generated/torch.stack.html|torch.stack]] || Concatenate a list of tensors along a new dimension ||
|| [[https://pytorch.org/docs/stable/generated/torch.cat.html|torch.cat]] || Concatenate a list of tensors along an existing dimension ||
|| [[https://pytorch.org/docs/stable/generated/torch.sum.html|torch.sum]] || Sum a tensor along one or more dimensions ||
|| [[https://pytorch.org/docs/stable/generated/torch.mean.html|torch.mean]] || Average a tensor along one or more dimensions ||
|| [[https://pytorch.org/docs/stable/generated/torch.norm.html|torch.norm]] || Calculate the norm of tensor along the given dimensions ||
|| [[https://pytorch.org/docs/stable/generated/torch.where.html|torch.where]] || Return a new tensor containing values from one of two sources depending on a condition ||
|| [[https://pytorch.org/docs/stable/generated/torch.topk.html|torch.topk]] || Get top k largest or smallest values and their indices from a tensor ||
|| [[https://pytorch.org/docs/stable/generated/torch.sigmoid.html|torch.sigmoid]] || Apply the sigmoid function to all values of a tensor ||
|| [[https://pytorch.org/docs/stable/generated/torch.softmax.html|torch.softmax]] || Perform softmax normalization ||
|| [[https://pytorch.org/docs/stable/generated/torch.index_select.html|torch.index_select]] || Select indices from a tensor along a specific dimension ||
|| [[https://pytorch.org/docs/stable/generated/torch.arange.html|torch.arange]] || Create a tensor from a range ||
|| [[https://pytorch.org/docs/stable/generated/torch.multinomial.html|torch.multinomial]] || Sample from a multinomial distribution ||