AI Model Training Project

This project demonstrates a complete machine learning workflow from data preparation to model deployment, using the MNIST dataset with an innovative approach to digit recognition.

Project Structure

.
├── data/                  # Dataset storage
├── models/               # Saved model files
├── src/                  # Source code
│   ├── data_preparation.py
│   ├── model.py
│   ├── training.py
│   ├── evaluation.py
│   └── deployment.py
├── notebooks/           # Jupyter notebooks for exploration
├── requirements.txt     # Project dependencies
└── README.md           # Project documentation

Setup Instructions

Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Install dependencies:

pip install -r requirements.txt

Run the training pipeline:

python src/training.py

Project Features

Custom CNN architecture for robust digit recognition
Data augmentation techniques
Model evaluation and hyperparameter tuning
Model deployment pipeline
Performance monitoring

Learning Concepts Covered

Data Preprocessing
- Data loading and cleaning
- Feature engineering
- Data augmentation
Model Architecture
- Custom CNN design
- Layer configuration
- Activation functions
Training Process
- Loss functions
- Optimizers
- Learning rate scheduling
- Early stopping
Evaluation
- Metrics calculation
- Cross-validation
- Model comparison
Deployment
- Model saving
- Inference pipeline
- Performance monitoring