π Deep Statistical Learning
π Full repository: deep_statistical_learning
I started writing these notes during my masterβs 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 β 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:
- β¨ Build strong intuition β explain mathematical ideas in clear, accessible language.
- π§© Connect theory and practice β link statistical concepts to modern deep learning methods.
- π Serve as a long-term reference β 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.