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+---
+Title: '.exp()'
+Description: 'Returns a new tensor with the exponential of each element in the input tensor'
+Subjects:
+  - 'Computer Science'
+  - 'Machine Learning'
+Tags:
+  - 'Functions'
+  - 'Machine Learning'
+  - 'Python'
+  - 'Tensor'
+CatalogContent:
+  - 'intro-to-py-torch-and-neural-networks'
+  - 'paths/computer-science'
+---
+
+In PyTorch, the **`.exp()`** function computes the exponential of each element in the input tensor. This is mathematically equivalent to applying the function $y_i = e^{x_i}$ element-wise, where `e` is Euler's number (approximately 2.71828)).
+
+## Syntax
+
+```pseudo
+torch.exp(input, *, out=None) → Tensor
+```
+
+**Parameters:**
+
+- `input`: The input tensor containing elements for which the exponential will be computed.
+- `out` (optional): A tensor to store the output. If provided, the result is written to this tensor.
+
+**Return value:**
+
+Returns a new tensor where each element is the exponential of the corresponding element in the input tensor.
+
+## Example
+
+In this example, we compute the element-wise exponential of a tensor using `torch.exp()`:
+
+```py
+import torch
+import math
+
+# Define a tensor
+x = torch.tensor([0.0 , 1.0 , 2.0 , math.log(2.) ])
+
+# Compute the exponential
+result = torch.exp(x)
+
+print(result)
+```
+
+The above code produces the following output:
+
+```shell
+tensor([1.0000 , 2.7183, 7.3891 , 2. ])
+```