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README.md

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+---
+language: en
+license: mit
+library_name: pytorch
+tags:
+- mnist
+- image-classification
+- neural-network
+datasets:
+- mnist
+metrics:
+- accuracy
+---
+
+# Simple PyTorch Neural Network for MNIST
+
+This model is a basic feed-forward neural network trained on the MNIST dataset as part of a PyTorch tutorial.
+
+## Model Architecture
+The model consists of:
+1. **Input Layer**: 784 neurons (28x28 flattened images).
+2. **Hidden Layer**: 128 neurons with ReLU activation.
+3. **Output Layer**: 10 neurons (one for each digit from 0-9).
+
+## Training Details
+- **Dataset**: MNIST (60,000 training images, 10,000 test images)
+- **Epochs**: 5 (by default)
+- **Optimizer**: Adam (lr=0.001)
+- **Loss Function**: CrossEntropyLoss
+
+## Usage
+To load this model in your PyTorch project:
+
+```python
+import torch
+from simple_nn import SimpleNN
+
+# 1. Initialize the model architecture
+model = SimpleNN()
+
+# 2. Load the state dictionary
+model.load_state_dict(torch.load("model.pth"))
+model.eval()
+```
+
+## Dataset Information
+The MNIST dataset consists of 28x28 grayscale images of the 10 digits. It is a classic dataset for image classification tasks.