https://github.com/g-wtham/handwritten-digit-classification-with-mnist

Explore the MNIST dataset, consisting of handwritten digit images, and aim to improve the performance of a neural network model for digit classification
https://github.com/g-wtham/handwritten-digit-classification-with-mnist

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Explore the MNIST dataset, consisting of handwritten digit images, and aim to improve the performance of a neural network model for digit classification

• Host: GitHub
• URL: https://github.com/g-wtham/handwritten-digit-classification-with-mnist
• Owner: g-wtham
• Created: 2024-02-26T12:53:34.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2024-02-26T12:56:33.000Z (12 months ago)
• Last Synced: 2024-02-26T13:58:13.919Z (12 months ago)
• Language: Jupyter Notebook
• Size: 74.2 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## Handwritten Digit Classification with MNIST Dataset

Explore the MNIST dataset, consisting of handwritten digit images, and aim to improve the performance of a neural network model for digit classification

In this project, we explore the MNIST dataset, consisting of handwritten digit images, and aim to improve the performance of a neural network model for digit classification. We start by understanding the dataset and its characteristics and then proceed to build and optimize a neural network model using TensorFlow and Keras.

**Problem Statement:**

The goal is to achieve high accuracy in classifying handwritten digits from the MNIST dataset using a neural network model. We aim to improve the model's performance through various techniques such as adjusting network architecture, activation functions, and optimization algorithms.

**Scope:**

1. Understand the MNIST dataset and its structure.

2. Build a neural network model for digit classification.

3. Analyze model performance and identify areas for improvement.

4. Experiment with different network layers, activation functions, and optimization algorithms to enhance model accuracy.

5. Evaluate and compare the performance of different model configurations.

**Dependencies:**

- TensorFlow

- Keras

- Matplotlib

- NumPy

**Methodology:**

1. Loading and Preprocessing Data:

   - Load the MNIST dataset using Keras.

   - Preprocess the data by scaling pixel values to the range [0, 1].

2. Building Baseline Model:

   - Create a simple neural network model with a single dense layer.

   - Compile the model with appropriate loss function and optimizer.

3. Training Baseline Model:

   - Train the baseline model on the MNIST training data.

   - Evaluate the model's performance on the test dataset.

4. Analyzing Baseline Model Performance:

   - Examine accuracy and loss metrics to assess model performance.

   - Visualize model predictions and analyze misclassifications.

5. Improving Model Performance:

   - Experiment with different network architectures (e.g., adding hidden layers, adjusting layer sizes).

   - Explore alternative activation functions (e.g., ReLU, sigmoid) and optimization algorithms (e.g., Adam, SGD) to optimize model training.

   - Fine-tune hyperparameters such as learning rate, batch size, and number of epochs.

6. Evaluating Enhanced Models:

   - Train and evaluate enhanced models with optimized configurations.

   - Compare performance metrics with the baseline model.

**Future Work:**

1. Further exploration of advanced techniques such as regularization and dropout.

2. Integration of convolutional neural networks (CNNs) for image classification tasks.

3. Deployment of the optimized model for real-world applications.