[Term Entry] PyTorch Tensor Operations: .argmin() (#8226)

* Add entry for PyTorch .argmin() tensor operation

* Update argmin.md with clarifications and examples

Clarified the behavior of the .argmin() method regarding tensor flattening and return values. Added details about parameters and return value for better understanding.

* Update description of .argmin() method

Removed mention of returning indices along a specified dimension in the description of the .argmin() method.

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Author: kim081890

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

@@ -0,0 +1,68 @@
+---
+Title: '.argmin()'
+Description: 'Returns the index of the minimum value in a PyTorch tensor, or along a specified dimension.'
+Subjects:
+  - 'Computer Science'
+  - 'Data Science'
+Tags:
+  - 'Deep Learning'
+  - 'Methods'
+  - 'PyTorch'
+  - 'Tensor'
+CatalogContent:
+  - 'intro-to-py-torch-and-neural-networks'
+  - 'paths/data-science'
+---
+
+The **`.argmin()`** method in PyTorch returns the index of the minimum value in a flattened [tensor](https://www.codecademy.com/resources/docs/pytorch/tensors) tensor by default, or along a specified dimension. This method is commonly used in tasks such as finding the closest data point, selecting the best prediction, or identifying the least likely class in machine learning workflows.
+
+## Syntax
+
+```pseudo
+torch.argmin(input, dim=None, keepdim=False)
+```
+
+**Parameters:**
+
+- `input` (Tensor): The input tensor to search for the minimum value.
+- `dim` (int, optional): The dimension to reduce. If not specified, the index of the minimum value in the flattened tensor is returned.
+- `keepdim` (bool, optional): Whether the output tensor retains the reduced dimension. Defaults to `False`.
+
+**Return value:**
+
+The `.argmin()` method returns a `LongTensor` containing the index or indices of the minimum value(s). If `dim` is not specified, a scalar tensor is returned.
+
+## Example
+
+This example shows how to use the `.argmin()` method to find the index of the minimum value in a 2D tensor:
+
+```py
+import torch
+
+# Define a 2D tensor
+tensor = torch.tensor([[8, 3, 5],
+                       [2, 7, 4]])
+
+# Index of minimum in flattened tensor
+print(torch.argmin(tensor))
+
+# Index of minimum along each column (dim=0)
+print(torch.argmin(tensor, dim=0))
+
+# Index of minimum along each row (dim=1)
+print(torch.argmin(tensor, dim=1))
+```
+
+This example results in the following output:
+
+```shell
+tensor(3)
+tensor([1, 0, 1])
+tensor([1, 0])
+```
+
+In this example:
+
+- **Flattened tensor**: The tensor is treated as `[8, 3, 5, 2, 7, 4]`, and the minimum value `2` is at index `3`.
+- **Along columns (`dim=0`)**: The minimum values in each column are `2`, `3`, and `4`, found in rows `1`, `0`, and `1`.
+- **Along rows (`dim=1`)**: The minimum values in each row are `3` (at index `1`) and `2` (at index `0`).