[Edit] Python (Pytorch): .cat()

content/pytorch/concepts/tensors/terms/cat/cat.md

@@ -1,36 +1,41 @@
 ---
 Title: '.cat()'
-Description: 'Concatenates two or more tensors in the same dimension.'
+Description: 'Concatenates two or more tensors along a specified dimension.'
 Subjects:
-  - 'AI'
+  - 'Computer Science'
   - 'Data Science'
 Tags:
   - 'AI'
   - 'Deep Learning'
   - 'Functions'
   - 'Machine Learning'
+  - 'PyTorch'
 CatalogContent:
   - 'intro-to-py-torch-and-neural-networks'
   - 'py-torch-for-classification'
 ---
 
-The **`.cat()`** function in PyTorch concatenates two or more tensors along a specified dimension. The tensors must have the same shape in all dimensions except for the dimension along which they are concatenated.
+The **`.cat()`** function in PyTorch concatenates two or more [tensors](https://www.codecademy.com/resources/docs/pytorch/tensors) along a specified dimension. The tensors must have the same shape in all dimensions except for the dimension along which they are concatenated.
 
 ## Syntax
 
 ```pseudo
 torch.cat(tensors, dim=0, out=None)
 ```
 
-## Parameters
+**Parameters:**
 
 - `tensors`: A sequence (like a list or tuple) of tensors to be concatenated. All tensors must have the same shape in all dimensions except for the specified dimension.
 - `dim`: An integer specifying the dimension along which the tensors will be concatenated. The default value is `0`, which means concatenation will occur along the first dimension.
 - `out`: A pre-allocated tensor with the correct shape to store the result of the concatenation. If not provided, a new tensor will be allocated.
 
-## Example 1
+**Return value:**
+
+A new tensor resulting from concatenating the input tensors along the specified dimension.
+
+## Example 1: Concatenating tensors along the first dimension
 
-The example below showcases concatenating tensors along the first dimension using the `.cat()` function:
+This demonstration highlights how to combine tensors along the first axis (dimension 0) with the help of the `.cat()` method:
 
 ```py
 import torch
@@ -70,9 +75,9 @@ tensor([[ 1,  2,  3],
         [10, 11, 12]])
 ```
 
-## Example 2
+## Example 2: Stacking Tensors Across the Second Axis
 
-The example below showcases concatenating tensors along the second dimension using the `.cat()` function:
+In this case, tensors are joined along the second dimension (axis 1) using the `.cat()` function to demonstrate horizontal concatenation:
 
 ```py
 import torch
@@ -110,9 +115,9 @@ tensor([[ 1,  2,  3,  7,  8,  9],
         [ 4,  5,  6, 10, 11, 12]])
 ```
 
-## Example 3
+## Example 3: Concatenating tensors along the third dimension
 
-The example below showcases concatenating tensors along the third dimension using the `.cat()` function:
+This example shows how to append tensors along the third dimension (axis 2), effectively stacking them in depth using the `.cat()` method:
 
 ```py
 import torch
@@ -158,3 +163,20 @@ tensor([[[ 1,  2,  3, 13, 14, 15],
         [[ 7,  8,  9, 19, 20, 21],
          [10, 11, 12, 22, 23, 24]]])
 ```
+
+## Frequently Asked Questions
+
+### 1. What is the difference between `.stack()` and `.cat()`?</summary>
+
+- `.cat()` combines tensors along an existing dimension.</li>
+- `.stack()` adds a new dimension and stacks tensors along that new axis.</li>
+
+Use stack when you want to create a new level of nesting; use cat to extend an existing one.
+
+### 2. Can I concatenate tensors of different data types or devices?
+
+No. All tensors must have the same data type and must be on the same device (e.g., all on [CPU](https://www.codecademy.com/resources/blog/what-is-a-cpu/) or all on GPU). Mismatches will raise an error.
+
+### 3. Can I concatenate along any dimension?
+
+Yes, as long as all other dimensions match. You can concatenate along any valid axis that exists in the input tensors.