README.md

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

1. Create a virtual environment:

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

2. Install dependencies:

pip install -r requirements.txt

3. 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

1. Data Preprocessing

   • Data loading and cleaning
   • Feature engineering
   • Data augmentation

2. Model Architecture

   • Custom CNN design
   • Layer configuration
   • Activation functions

3. Training Process

   • Loss functions
   • Optimizers
   • Learning rate scheduling
   • Early stopping

4. Evaluation

   • Metrics calculation
   • Cross-validation
   • Model comparison

5. Deployment

   • Model saving
   • Inference pipeline
   • Performance monitoring