https://github.com/codeofrahul/digit_classification_using_cnn

This project demonstrates the implementation of a Convolutional Neural Network (CNN) for classifying handwritten digits using the MNIST dataset. The model is built using Keras and TensorFlow, leveraging the power of deep learning for image recognition tasks.
https://github.com/codeofrahul/digit_classification_using_cnn

keras matplotlib python tensorflow

Last synced: 8 days ago
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This project demonstrates the implementation of a Convolutional Neural Network (CNN) for classifying handwritten digits using the MNIST dataset. The model is built using Keras and TensorFlow, leveraging the power of deep learning for image recognition tasks.

• Host: GitHub
• URL: https://github.com/codeofrahul/digit_classification_using_cnn
• Owner: CodeofRahul
• License: mit
• Created: 2025-01-22T09:11:28.000Z (8 days ago)
• Default Branch: main
• Last Pushed: 2025-01-22T09:37:09.000Z (8 days ago)
• Last Synced: 2025-01-22T10:27:05.665Z (8 days ago)
• Topics: keras, matplotlib, python, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 1.4 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Digit_Classification_using_CNN

This project demonstrates the implementation of a Convolutional Neural Network (CNN) for classifying handwritten digits using the MNIST dataset. The model is built using Keras and TensorFlow, leveraging the power of deep learning for image recognition tasks.

## Key Features

- **Data Preprocessing:** The MNIST dataset is loaded and preprocessed, including reshaping and min-max scaling, to optimize model training.

- **CNN Architecture:** A carefully designed CNN architecture is employed, incorporating convolutional, pooling, and fully connected layers to extract features and perform classification.

- **Model Training and Evaluation:** The model is trained on the training data and evaluated on the testing data to assess its performance. Accuracy and loss metrics are used to gauge the model's effectiveness.

- **Visualization:** Matplotlib is used to visualize the handwritten digits and display the model's predictions.

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## 📊 Dataset

The **MNIST dataset** consists of 70,000 grayscale images of handwritten digits (0-9):

- **Training Set:** 60,000 images

- **Test Set:** 10,000 images

Each image is a 28x28 pixel matrix, with each pixel representing grayscale intensity.

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## 🚀 Methodology

1. **Data Preprocessing:**

   - Normalized pixel values to a range of 0 to 1.

   - Reshaped images to include the channel dimension (28x28x1).

   - One-hot encoded the labels.

2. **Model Architecture:**

   - Convolutional Layers: Extract spatial features using 32 and 64 filters.

   - Pooling Layers: Reduce spatial dimensions and computational complexity.

   - Dense Layers: Perform high-level reasoning for classification.

   - Softmax Output: Predicts probabilities for each class.

3. **Training:**

   - **Loss Function:** Categorical Cross-Entropy.

   - **Optimizer:** sgd.

   - **Metrics:** Accuracy.

4. **Evaluation:**

   - Assessed model performance on the test set.

   - Visualized misclassified examples for deeper insights.

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## 📈 Results

- **Training Accuracy:** 99%

- **Validation Accuracy:** 98%

- **Test Accuracy:** 98%