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Achievement: Pull Sharkx3Achievement: YOLOAchievement: QuickdrawAchievement: Pair Extraordinaire

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OscarSavolainen/README.md

🏄‍♂️ OscarSavolainenDR

Machine Learning Research Engineer

My 3 great engineering loves are analyzing data, neural network quantization for edge devices and GPUs, and building ML dev tools. At the risk of sounding like a complete nerd, I am super into working on ML tooling that solves complex problems for devs, giving good control but abstracting away the difficulties. I find it very emotionally rewarding, since one is encouraged to write nice code and documentation that works on cutting-edge ML problems. At the moment, my main open-source work involves being a co-creator and main contributor to alma, a library for optimizing PyTorch inference, and working on state-of-the-art neural network quantization algorithms.

🧰 Languages and Tools

PyTorch

TensorFlow

Git

Linux

Python

GitHub

Rust

C++

Golang

Bash

Lua

Docker

HTML

CSS

JavaScript

React


📺 Latest YouTube Videos

Alma: Free Tool to Speed Up your PyTorch Model | Auto-Benchmark 50+ Conversion Options GPTQ Quantization EXPLAINED Speedrun deploying LLM Embedding models into Production Cross Layer Equalization: Everything You Need to Know How to see inside Neural Networks: New Tensor Histogram and Jacobian Sensitivity Analysis Tool! Quantization Aware Training (QAT) With a Custom DataLoader: Beginner's Tutorial to Training Loops

📊 Stats

GitHub Streak

👨‍💻 Oscar's Coding Journey

 I first got started in coding as a means of enabling me to do what I love: data analysis. I go cuckoo for data, and coding was a way to enable gathering, transforming, and visualizing numbers. Over time I ended up using more and more advanced techniques. When I was doing my PhD in neurotechnology at Imperial College, to tackle complex biological data, I had to start getting the big algorithms involved: Machine Learning. Before my PhD even ended, I started working professionally as an ML Researcher, and grew to love ML for itself: understanding how it learns transforms, the subtleties of forward and backwards passes, and most of all, how it reacts when we throw a sackful of wrenches into the motor of the algorithm when we do quantization. At the moment, I'm excited to be educating others on neural network quantization and building my own quantization library, while continuing my journey of diving down into computational optimization, low-level languages such as Rust, and playing with various LLM use cases.

Pinned Loading

  1. Inter-Frequency-Power-Correlation-Statistical-Significance-Test Inter-Frequency-Power-Correlation-Statistical-Significance-Test Public

    A Monte-Carlo based statistical significance test for inter-frequency power correlations in non-stationary time-series. Accounts for intra-frequency autocorrelation, inter-frequency non-dyadicity, …

    MATLAB 2 1

  2. Quantization-Tutorials Quantization-Tutorials Public

    A bunch of coding tutorials for my Youtube videos on Neural Network Quantization.

    Jupyter Notebook 14 6

  3. pytorch pytorch Public

    Forked from pytorch/pytorch

    Tensors and Dynamic neural networks in Python with strong GPU acceleration

    Python

  4. AutomatedFilesBackup AutomatedFilesBackup Public

    Easy, automated, scheduled backup to Github of arbitrary, distributed Linux files

    Shell

  5. EasyQuant EasyQuant Public

    A minimalist extension to PyTorch's quantization library, which improves QAT training speed, adds custom visual quantization debugging tools, and robust unit testing. Natively compatible with both …

    Jupyter Notebook 3 1

  6. alma alma Public

    Forked from saifhaq/alma

    Python