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+---
+Title: '.max()'
+Description: 'Returns the maximum value of a tensor, either across the entire 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 **`.max()`** method in PyTorch returns the maximum value from a [tensor](https://www.codecademy.com/resources/docs/pytorch/tensors). It can find the maximum value across the entire tensor or along a specified dimension. This method is commonly used in data analysis, finding peak values, and various neural network operations.
+
+## Syntax
+
+```pseudo
+torch.max(input, dim=None, keepdim=False, *, out=None) → Tensor or (Tensor, LongTensor)
+```
+
+**Parameters:**
+
+- `input` (Tensor): The input tensor.
+- `dim` (int, optional): The dimension along which to find the maximum values. If not specified, returns the maximum value of the entire tensor.
+- `keepdim` (bool, optional): Whether the output tensor retains the reduced dimension. Defaults to `False`.
+- `out` (tuple of Tensors, optional): Optional tuple of two tensors to store the output values and indices.
+
+**Return value:**
+
+- When `dim` is not specified, it returns a tensor containing the single maximum value from the entire tensor.
+- When `dim` is specified, it returns a named tuple `(values, indices)` where `values` contains the maximum values along the specified dimension, and `indices` contains the indices of those maximum values.
+
+## Example
+
+The following example demonstrates how to use the `.max()` method to find the maximum value in a tensor:
+
+```py
+import torch
+
+# Create a 2D tensor
+tensor = torch.tensor([[1.5, -2.3, 0.0],
+                       [4.8, -1.2, 3.6]])
+
+# Find the maximum value of the entire tensor
+max_value = torch.max(tensor)
+
+# Find maximum values along each column (dim=0)
+max_cols = torch.max(tensor, dim=0)
+
+# Find maximum values along each row (dim=1)
+max_rows = torch.max(tensor, dim=1)
+
+print("Original Tensor:")
+print(tensor)
+
+print("\nMaximum Value (entire tensor):")
+print(max_value)
+
+print("\nMaximum Values (along columns, dim=0):")
+print("Values:", max_cols.values)
+print("Indices:", max_cols.indices)
+
+print("\nMaximum Values (along rows, dim=1):")
+print("Values:", max_rows.values)
+print("Indices:", max_rows.indices)
+```
+
+This example results in the following output:
+
+```shell
+Original Tensor:
+tensor([[ 1.5000, -2.3000,  0.0000],
+        [ 4.8000, -1.2000,  3.6000]])
+
+Maximum Value (entire tensor):
+tensor(4.8000)
+
+Maximum Values (along columns, dim=0):
+Values: tensor([4.8000, -1.2000, 3.6000])
+Indices: tensor([1, 1, 1])
+
+Maximum Values (along rows, dim=1):
+Values: tensor([1.5000, 4.8000])
+Indices: tensor([0, 0])
+```
+
+In this example:
+
+- **Entire tensor**: The maximum value across all elements is `4.8000`.
+- **Along columns (`dim=0`)**: The maximum values in each column are `4.8000`, `-1.2000`, and `3.6000`, all found in row `1` (index `1`).
+- **Along rows (`dim=1`)**: The maximum values in each row are `1.5000` (at index `0`) and `4.8000` (at index `0`).
+- When `dim` is specified, the method returns both the maximum values and their indices as a named tuple.