ML-Core is a modular and extensible framework designed to accelerate Machine Learning (ML) and Deep Learning (DL) development.
It provides reusable components for model training, evaluation, optimization, and data preprocessing, enabling engineers and researchers to focus on building meaningful, high-impact solutions.
The goal of ML-Core is to create a clean, reproducible, and scalable foundation for applied machine learning.
It serves as a sandbox for experimentation, research, and deployment of models that bridge theory and production.
| Module | Description |
|---|---|
| core/ | Fundamental ML utilities and base model architecture |
| datasets/ | Example datasets and preprocessing pipelines |
| models/ | TensorFlow, PyTorch, and Scikit-Learn example models |
| evaluation/ | Evaluation metrics and visualization tools |
| notebooks/ | Jupyter notebooks for EDA and training experiments |
“Whatever you do, if what you’re doing doesn’t positively impact someone else’s life, you’re wasting your time.”
— Ahmet Ruçhan Avcı
📌 Key Resources
- 📘 TensorFlow Documentation
- 🔥 PyTorch Documentation
- ⚙️ Scikit-Learn Guide
- 🧩 Artificial Neural Networks — Comprehensive Guide
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