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Achievement: YOLOAchievement: Pull Sharkx2Achievement: Arctic Code Vault Contributor

Achievements

Achievement: YOLOAchievement: Pull Sharkx2Achievement: Arctic Code Vault Contributor

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Sillians/README.md

Painstakingly Overfitting on a Learning Curve.

I don’t just train models; I build the systems that make them viable. My focus is on the structural integrity of ML|AI products, bridging the gap between a research breakthrough and a resilient, production-grade application. I take full ownership of the end-to-end lifecycle. To me, a model is only as good as the pipeline that feeds it and the infrastructure that serves it. I specialize in designing scalable MLOps frameworks and integrating Generative AI into functional software architectures.

I enjoy early morning runs and occasionally play basketball. Space enthusiast | Inspired by the Starship program!

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  1. designing-end-to-end-machine-learning-systems designing-end-to-end-machine-learning-systems Public

    Business and technical approaches to designing end-to-end machine learning systems from data ingestion to model deployment. Architecting scalable and population-reday ML pipelines and infrastructure.

  2. Building-LLMs-from-scratch Building-LLMs-from-scratch Public

    This repository guides you through the process of building a GPT-style Large Language Model (LLM) from scratch using PyTorch. The structure and approach are inspired by the book Build a Large Langu…

    Jupyter Notebook 1

  3. dl-llm-majors dl-llm-majors Public

    Explore the evolution of neural networks through this repository. You’ll start with the core building blocks and work your way up to implementing sophisticated models like GPT.

    Jupyter Notebook

  4. mathematics_for_ml mathematics_for_ml Public

    Core mathematics for machine learning that tackles concepts such as linear algebra, multivariable calculus, and probability & statistics. The linear algebra section covers crucial machine learning …

  5. Loss-Functions-in-Deep-Learning Loss-Functions-in-Deep-Learning Public

    Loss functions are at the heart of deep learning, shaping how models learn and perform across diverse tasks. They are used to quantify the difference between predicted outputs and ground truth labe…

    Python

  6. Optimization-Algorithms-in-Deep-Learning Optimization-Algorithms-in-Deep-Learning Public

    Training deep learning models involves solving an optimization problem, where the goal is to incrementally adapt the model to minimize an objective function. At the core of this process are optimiz…

    Python