📖 Deep Statistical Learning

👉 Full repository: deep_statistical_learning

I started writing these notes during my master’s degree as a way to organize and clarify my understanding of machine learning and deep learning. Over time, the notes have grown into a comprehensive study resource that combines theoretical foundations with practical insights.

I enjoy organizing knowledge and writing things down, and these notes reflect that process — carefully breaking down concepts, connecting ideas, and recording them in a way that can be revisited and built upon.

The goal of this collection is to:

• ✨ Build strong intuition — explain mathematical ideas in clear, accessible language.
• 🧩 Connect theory and practice — link statistical concepts to modern deep learning methods.
• 📚 Serve as a long-term reference — a resource I can return to as my research and projects evolve.

The notes cover a range of topics including probability and statistics, optimization methods, neural networks, and modern deep learning architectures, along with worked examples and derivations.

I welcome all comments and suggestions—and I’d be happy to improve and grow this note together with you.