This folder contains a Jupyter Notebook file for interactive exploration and experimentation with the MNIST image classification project.
This Jupyter Notebook provides an interactive environment where you can:
- Load and preprocess the MNIST dataset: Explore the data, visualize sample images, and perform necessary transformations.
- Build and compile the neural network model: Define the model's architecture, choose an optimizer, loss function, and metrics.
- Train the model: Execute the training process and monitor the model's performance.
- Evaluate the trained model: Calculate metrics like accuracy and loss on the test dataset.
- Generate predictions: Use the trained model to make predictions on new data.
- Visualize results: Create plots to understand the model's predictions, including visual comparisons of correct and incorrect classifications, and probability distributions of individual predictions.
- Experiment with different parameters: Modify the model's architecture, training settings, and hyperparameters to optimize performance.
- Document your steps: Add text cells to explain your code, document your findings, and create a comprehensive record of your exploration.
By utilizing this Jupyter Notebook, you can gain a deeper understanding of the MNIST image classification problem, explore different approaches to solve it, and document your journey in a clear and organized manner.