From 7a58eab3c8db95899b21a2873538cc1d2e6c2bfa Mon Sep 17 00:00:00 2001
From: AtulDeshpande09 <deshpandeatul05@gmail.com>
Date: Fri, 4 Jul 2025 19:06:38 +0530
Subject: [PATCH] [Term Entry] PyTorch Tensor Operations: .exp()

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+---
+Title: '.exp()'
+Description: 'Returns a new tensor with the exponential of the elements of the input tensor input..'
+Subjects:
+  - 'Computer Science'
+  - 'Machine Learning'
+Tags:
+  - 'Python'
+  - 'Machine Learning'
+  - 'Functions'
+  - '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 (~2.71828).
+
+## Syntax
+
+```py
+torch.exp(input, *, out=None) → Tensor
+```
+
+**Parameters :**
+
+- input: The input tensor.
+- out *(optional)*: The output tensor to store results.
+
+**Return Value :**
+
+A tensor with the same shape as `input`, where each element is converted from degrees to radians. If `out` is specified, the returned tensor is the same as `out`.
+
+Example : 
+```py
+import torch
+
+# 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. ])
+```
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