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+---
+Title: '.sigmoid()'
+<<<<<<< HEAD
+Description: 'The sigmoid function is an S-shaped curve typically used in binary classification problems'
+=======
+Description: 'Applies the sigmoid activation to each element of a tensor, mapping values to a range between 0 and 1'
+>>>>>>> c428da50f (Address maintainer feedback)
+Subjects:
+  - 'AI'
+  - 'Machine Learning'
+Tags:
+  - 'AI'
+  - 'Classification'
+  - 'Logistic Regression'
+  - 'Machine Learning'
+  - 'Math'
+  - 'Models'
+  - 'Neural Networks'
+  - 'Python'
+  - 'PyTorch'
+CatalogContent:
+  - 'py-torch-for-classification'
+  - 'intro-to-py-torch-and-neural-networks'
+---
+
+<<<<<<< HEAD
+A **`.sigmoid()`** function is an S-shaped curve which maps any real-valued input to a bounded output, typically between 0 and 1. Sigmoid functions are regularly used as activation functions in non-linear classification problems, for example in neural networks, where the probability of a binary outcome is required.
+=======
+The .sigmoid() function applies the sigmoid (logistic) function to each element of a tensor, producing an S-shaped curve that maps any real-valued input to a value between 0 and 1.
+>>>>>>> c428da50f (Address maintainer feedback)
+
+In machine learning, sigmoid is commonly used as an activation function in binary classification tasks, where outputs represent probabilities."
+
+The formula for the sigmoid function is given by:
+$$σ(x) = 1 / (1 + e^(-x))$$
+
+## Example
+
+The following example plots the sigmoid function:
+
+```py
+import matplotlib.pyplot as plt
+import torch
+
+x = torch.linspace(-10, 10, steps=400)
+y = torch.sigmoid(x)
+
+plt.plot(x.numpy(), y.numpy())
+plt.title("Sigmoid function")
+plt.xlabel("x")
+plt.ylabel("σ(x)")
+plt.show()
+```
+
+This example applies the sigmoid function to a tensor:
+
+```py
+import torch
+
+x = torch.tensor([-1.8, -1.5, 0.0, 2.0, 4.0])
+y = torch.sigmoid(x)
+
+print(y)
+```
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