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NathanP23/README.md

🚧 WARNING: UNDER CONSTRUCTION 🚧

⚠️ VISITOR WARNING ⚠️
The repositories below are primarily academic artifacts.
They are incomplete, unmaintained, and held together by duct tape and student tears.
Do not expect production code. Expect "it worked on my machine 5 minutes before the deadline" code.

👋 Hi, I’m Nathan

I am navigating the messiness of real-world data and the abstract theory of deep learning.

🎓 Data Science Student @ The Hebrew University of Jerusalem
🔬 Research Data Scientist @ Israel Central Bureau of Statistics (CBS) (Specifically the Statistical Methodology Department, where I research Deep learning and Explainable AI (xAI) for national data)

🔭 Current Focus

My academic focus has shifted from standard ML & stats to Generative Models, Deep Learning, Computer Graphics, and Image Processing. I am currently breaking things in:

  • Deep Generative Models: Diffusion, GANs, and Autoencoders.
  • Image Processing & Computer Graphics: 3D vision concepts and heavy image processing pipelines.
  • xAI Research: Trying to make black-box models explain themselves in a government context mathematically.

🚧 Active Construction Zones (Newer Coursework)

These projects are currently being built (or broken) as part of my advanced electives.

  • Generative Models: Exploring Diffusion Models, VAEs, and GAN architectures.
  • Image Processing: Low-level vision, filtering, and frequency domain manipulation.
  • Computer Graphics: 3D rendering pipelines, geometry processing, and ray tracing.

🏛️ The Graveyard (Past Academic Repositories)

Most of these are frozen in time. They served their purpose for a grade and have been abandoned since.

Deep Learning & Neural Networks

  • Intro to Deep Learning (67822): My sandbox for PyTorch implementations. Contains custom Convolutional Autoencoders (CAEs), Transfer Learning experiments, and from-scratch implementations of RNNs and Attention mechanisms.

Statistical Foundations

🛠️ The Toolkit

  • Languages: Python (Native), SQL (Fluent), Bash (Love it), C/C++ (where i started - Native)
  • Frameworks: PyTorch (Daily driver), scikit-learn, pandas, NumPy, MatPlotLib
  • Infrastructure: Docker, FastAPI, Redis
  • Tools: JupyterLab, Git

🤝 Collaborative Interests

If you are okay with experimental code, I am open to:

  • Deep learning research (specifically PyTorch/Transformers)
  • Architectural innovation in Neural Networks
  • Applied ML pipelines that need to scale

I'm really busy right now finishing my degree...

⚡ Fun Fact

I play guitar and listen to Heavy Metal. Much like the current GitHub state, it is loud, complex, and sometimes difficult to understand.

Pinned Loading

  1. Introduction-to-Deep-Learning-67822 Introduction-to-Deep-Learning-67822 Public

    Exercises from the course "Introduction to Deep Learning (67822)" at The Hebrew University of Jerusalem, in the Department of Computer Science and Engineering.

    Jupyter Notebook 1

  2. Big-Data-Mining-52002 Big-Data-Mining-52002 Public

    Midterm and Final assignments of the course "Big Data Mining (52002)" at The Hebrew University of Jerusalem, in the Department of Statistics and Data Science. Focuses on analyzing massive datasets …

    Jupyter Notebook 1

  3. Statistical-Learning-and-Data-Analysis-52525 Statistical-Learning-and-Data-Analysis-52525 Public

    Exercises from the course "Statistical Learning and Data Analysis (52525)" at The Hebrew University of Jerusalem, in the Department of Statistics and Data Science.

    Jupyter Notebook 1

  4. Data-Analysis-with-R-52414 Data-Analysis-with-R-52414 Public

    Labs and Final assignments from the course "Data Analysis with R (52414)" at The Hebrew University of Jerusalem, in the Department of Statistics and Data Science.

    1

  5. Regression-and-Statistical-Models-52571 Regression-and-Statistical-Models-52571 Public

    Content of the course "Regression and Statistical Models (52571)" at The Hebrew University of Jerusalem, in the Department of Statistics and Data Science.

    Jupyter Notebook 1

  6. Principles-and-Applications-in-Stat-Analysis-52221 Principles-and-Applications-in-Stat-Analysis-52221 Public

    Content from the course "Principles and Applications in Statistical Analysis (52221)" at The Hebrew University of Jerusalem, in the Department of Statistics and Data Science.

    Jupyter Notebook 1