Machine Learning Classifier

A comprehensive deep learning project implementing convolutional neural networks for image classification tasks.

Problem Brief

What: Develop a high-accuracy image classification system using deep learning techniques.

Why: Address the need for automated image recognition in various domains including medical imaging, autonomous vehicles, and content moderation.

Success Criteria:

• Achieve >95% accuracy on test set
• Real-time inference capabilities
• Deploy as accessible web service

Results

Key Metrics

Achievements

• ✅ Exceeded accuracy target
• ✅ Real-time performance achieved
• ✅ Successful web deployment
• ✅ Comprehensive evaluation completed

Reproduce

Prerequisites

• Python 3.8+
• TensorFlow 2.x
• CUDA-capable GPU (recommended)

Local Development

git clone https://github.com/diazmarquez/ml-classifier
cd ml-classifier
pip install -r requirements.txt
python train.py

Demo

Interactive demo showcasing real-time image classification capabilities.

Features:

• Upload custom images
• Real-time predictions
• Confidence visualization
• Model interpretability

Try Live Demo

Technical Notes

This project demonstrates advanced deep learning techniques including transfer learning, data augmentation, and model optimization for production deployment.