[Term Entry]Py torch tensors .mean()

content/pytorch/concepts/tensors/mean/mean.md

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+---
+Title: '.mean()'
+Description: 'Calculates the mean of all elements in a PyTorch tensor or along a specified dimension.'
+Subjects:
+  - 'AI'
+  - 'Data Science'
+Tags:
+  - 'AI'
+  - 'Data Types'
+  - 'Deep Learning'
+  - 'Functions'
+CatalogContent:
+  - 'intro-to-py-torch-and-neural-networks'
+  - 'paths/data-science'
+---
+
+The **.mean()** method in PyTorch computes the arithmetic mean (average) of tensor elements. It can calculate the mean for all elements in the tensor or along a specified dimension. This method is widely used in data preprocessing and analysis for summarizing data.
+
+## Syntax
+
+```pseudo
+tensor.mean(dim=None, keepdim=False)
+```
+
+- `dim` (optional): The dimension along which the mean is computed. If not specified, the mean of all elements is calculated.
+- `keepdim` (optional): If `True`, retains the reduced dimension with size `1`. Defaults to `False`.
+
+The function returns a tensor containing the mean value(s).
+
+## Example
+
+This example demonstrates calculating the mean of all elements in a tensor and along a specific dimension:
+
+```py
+import torch
+
+# Create a tensor
+tensor = torch.tensor([[1.0, 2.0], [3.0, 4.0]])
+
+# Calculate the mean of all elements
+mean_all = tensor.mean()
+
+# Calculate the mean along dimension 0 (columns)
+mean_dim0 = tensor.mean(dim=0)
+
+print("Mean of all elements:", mean_all)
+print("Mean along dimension 0:", mean_dim0)
+```
+
+This example results in the following output:
+
+```shell
+Mean of all elements: tensor(2.5000)
+Mean along dimension 0: tensor([2.0000, 3.0000])
+```
+
+In this example:
+
+- `mean_all` computes the mean of all elements in the tensor.
+- `mean_dim0` computes the mean along each column (dimension 0), reducing the rows. This makes `.mean()` a versatile tool for data analysis.