From 1ba32fc17c7513e6f7081147851541ae111ed498 Mon Sep 17 00:00:00 2001
From: Amy Page <amyedp123@outlook.com>
Date: Tue, 16 Dec 2025 21:42:05 +0000
Subject: [PATCH 1/4] Add new description of sigmoid function in sigmoid.md

---
 .../terms/sigmoid/sigmoid.md                  | 45 +++++++++++++++++++
 1 file changed, 45 insertions(+)
 create mode 100644 content/pytorch/concepts/tensor-operations/terms/sigmoid/sigmoid.md

diff --git a/content/pytorch/concepts/tensor-operations/terms/sigmoid/sigmoid.md b/content/pytorch/concepts/tensor-operations/terms/sigmoid/sigmoid.md
new file mode 100644
index 00000000000..d296a05e761
--- /dev/null
+++ b/content/pytorch/concepts/tensor-operations/terms/sigmoid/sigmoid.md
@@ -0,0 +1,45 @@
+---
+Title: 'sigmoid'
+Description: 'The sigmoid function is an S-shaped curve typically used in binary classification problems'
+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'
+---
+
+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.
+
+Although there are a range of sigmoid functions, in the field of AI the sigmoid function is usually synonymous with the logistic function, which is bound between 0 and 1.
+
+The formula for the sigmoid function is given by:
+$$σ(x) = 1 / (1 + e^(-x))$$
+
+## Examples
+
+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()
+```

From 30dcd5a1db94cd8127a0bc83119528e2bec67edb Mon Sep 17 00:00:00 2001
From: Mamta Wardhani <mamta.wardhani@gmail.com>
Date: Wed, 17 Dec 2025 16:14:31 +0530
Subject: [PATCH 2/4] Revise title and description for sigmoid function

Updated the title to '.sigmoid()' and clarified the description of the sigmoid function.
---
 .../concepts/tensor-operations/terms/sigmoid/sigmoid.md       | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/content/pytorch/concepts/tensor-operations/terms/sigmoid/sigmoid.md b/content/pytorch/concepts/tensor-operations/terms/sigmoid/sigmoid.md
index d296a05e761..8a0e107c76e 100644
--- a/content/pytorch/concepts/tensor-operations/terms/sigmoid/sigmoid.md
+++ b/content/pytorch/concepts/tensor-operations/terms/sigmoid/sigmoid.md
@@ -1,5 +1,5 @@
 ---
-Title: 'sigmoid'
+Title: '.sigmoid()'
 Description: 'The sigmoid function is an S-shaped curve typically used in binary classification problems'
 Subjects:
   - 'AI'
@@ -19,7 +19,7 @@ CatalogContent:
   - 'intro-to-py-torch-and-neural-networks'
 ---
 
-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.
+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.
 
 Although there are a range of sigmoid functions, in the field of AI the sigmoid function is usually synonymous with the logistic function, which is bound between 0 and 1.
 

From 59f15ba3ee631e507de982c4502afffed471d0f7 Mon Sep 17 00:00:00 2001
From: Amy Page <amyedp123@outlook.com>
Date: Sat, 20 Dec 2025 12:33:15 +0000
Subject: [PATCH 3/4] Address maintainer feedback

---
 .../terms/sigmoid/sigmoid.md                  | 25 ++++++++++++++++---
 1 file changed, 22 insertions(+), 3 deletions(-)

diff --git a/content/pytorch/concepts/tensor-operations/terms/sigmoid/sigmoid.md b/content/pytorch/concepts/tensor-operations/terms/sigmoid/sigmoid.md
index 8a0e107c76e..e32427c65b8 100644
--- a/content/pytorch/concepts/tensor-operations/terms/sigmoid/sigmoid.md
+++ b/content/pytorch/concepts/tensor-operations/terms/sigmoid/sigmoid.md
@@ -1,6 +1,10 @@
 ---
 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'
@@ -19,14 +23,18 @@ CatalogContent:
   - '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)
 
-Although there are a range of sigmoid functions, in the field of AI the sigmoid function is usually synonymous with the logistic function, which is bound between 0 and 1.
+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:
 $$σ(x) = 1 / (1 + e^(-x))$$
 
-## Examples
+## Example
 
 The following example plots the sigmoid function:
 
@@ -37,9 +45,20 @@ import torch
 x = torch.linspace(-10, 10, steps=400)
 y = torch.sigmoid(x)
 
-plt.plot(x.numpy(), y.numpy()
+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)
+```
\ No newline at end of file

From f5321dbc1d9a74ae3ab559d2d44c269f8fe97ed5 Mon Sep 17 00:00:00 2001
From: Amy Page <amyedp123@outlook.com>
Date: Tue, 6 Jan 2026 22:54:59 +0000
Subject: [PATCH 4/4] Add example plot

---
 media/sigmoid-example.png | Bin 0 -> 19686 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 media/sigmoid-example.png

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