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content/pytorch/concepts/tensor-operations/terms/transpose/transpose.md

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+---
+Title: '.transpose()'
+Description: 'Reorders dimensions of a tensor by swapping two specified dimensions.'
+Subjects:
+  - 'AI'
+  - 'Data Science'
+Tags:
+  - 'AI'
+  - 'Arrays'
+  - 'Data Structures'
+  - 'Deep Learning'
+CatalogContent:
+  - 'intro-to-py-torch-and-neural-networks'
+  - 'paths/computer-science'
+---
+
+In PyTorch, the `.transpose()` operation creates a view of the input tensor with the specified dimensions swapped, commonly used for matrix operations and reshaping data for neural network inputs. Unlike `permute()`, `transpose()` only swaps two dimensions at a time.
+
+## Syntax
+
+```pseudo
+tensor.transpose(dim0, dim1)
+```
+
+- `dim0`: First dimension to be transposed
+- `dim1`: Second dimension to be transposed
+
+## Example
+
+The following code creates a 2x3 tensor with 2 rows and 3 columns, then uses `transpose()` to swap row and column dimensions, resulting in a 3x2 tensor where original rows become columns and vice versa:
+
+```py
+import torch
+
+# Create a 2x3 tensor and transpose its dimensions
+x = torch.tensor([[1, 2, 3],
+                 [4, 5, 6]])
+y = x.transpose(0, 1)
+print(y)
+```
+
+The above code will result in the following output:
+
+```shell
+tensor([[1, 4],
+        [2, 5],
+        [3, 6]])
+```