๐ Applied Mathematics student at USTH (University of Science and Technology of Hanoi) ๐ฌ AI / ML Researcher & Mathematical Software Engineer โ bridging rigorous mathematics and modern machine learning ๐ Building knowledge from first principles: derive mathematically, code from scratch, and verify empirically.
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22 ML & Deep Learning algorithms โ derived by hand, implemented in pure NumPy & PyTorch, verified by tests.
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The rest of the first-principles trilogy:
- ๐งฎ applied-mathematics-foundation โ the math prerequisites: Linear Algebra, Calculus, Probability & Statistics, Optimization, Information Theory, ODEs, Graph Theory, Numerical Methods
- ๐ first-principles-math-modeling โ 15 modeling topics: dynamical systems, SIR epidemics, game theory, LP/IP/KKT optimization, simulation โ 121 Colab-ready notebooks
- Mathematical Foundations of Deep Learning: Convex & non-convex optimization, matrix calculus, spectral theory.
- Neural ODEs & Dynamical Systems: Physics-Informed Neural Networks (PINNs), continuous-time models.
- First-Principles ML: Building transparent, scalable ML algorithms from scratch without black-box abstractions.
- Information & Optimization Theory: Variational inference, ELBO bounds, information geometry.
- Languages & Frameworks:
PythonPyTorchNumPySciPyPandasscikit-learn - Math & Authoring:
LaTeXKaTeXJupyterLabMatplotlibPlotly - DevOps & OS:
GitGitHub ActionsGitHub CLILinux (Bash)VS Code
"The purpose of computing is insight, not numbers." โ Richard Hamming