PyTorch nn Layers entry (#5586)

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+---
+Title: 'Layers'
+Description: 'Layers are modules that perform operations on input data to build neural networks.'
+Subjects:
+  - 'AI'
+  - 'Machine Learning'
+Tags:
+  - 'AI'
+  - 'Deep Learning'
+  - 'Machine Learning'
+  - 'Models'
+  - 'Neural Networks'
+CatalogContent:
+  - 'learn-intro-to-py-torch-and-neural-networks'
+  - 'py-torch-for-classification'
+  - 'paths/machine-learning'
+---
+
+**Layers** are modules that perform operations on input data to build neural networks.
+
+## Syntax
+
+By using PyTorch's `.Sequential()` method to build a neural network, we can specify layers and activation functions in sequence from input to output as shown below:
+
+```pseudo
+import torch
+import torch.nn as nn
+
+# Define the model for the neural network
+model = nn.Sequential(
+    # Define layers and activation functions here
+    nn.Linear(in_features, out_features),
+    nn.ReLU(),
+    nn.Linear(in_features, out_features)
+)
+```
+
+## Example
+
+Below is an example of a basic multi-layer neural network defined using PyTorch's `nn.Sequential()` method, where the data is processed through a specified sequence of layers and activation functions:
+
+```py
+import torch
+import torch.nn as nn
+
+model = nn.Sequential(
+    # First layer: Linear transformation from 56 input features to 128 output features
+    nn.Linear(56, 128),  # This layer learns to transform input data
+    nn.ReLU(),           # Activation function to introduce non-linearity
+
+    # Second layer: Linear transformation from 128 input features to 26 output features
+    nn.Linear(128, 26),  # This layer continues to transform the data
+    nn.ReLU(),           # Another activation function for non-linearity
+
+    # Third layer: Linear transformation from 26 input features to 1 output feature
+    nn.Linear(26, 1)     # Final output layer that produces the model's prediction
+)
+```