My Learning Journey in AI, Deep Learning, Research, and Open Source

Описание и применение

Студент второго курса CS изучает ИИ и глубокое обучение, завершил стажировку в MRM Research, проводит собственные исследования по интерпретируемости моделей, готовится к Google Summer of Code и публикует свои результаты.

Задача

A second‑year CS student seeks structured learning and contribution in AI, deep learning, and open‑source projects.

Результат

The student has transitioned from learning to active research participation and is positioning themselves for real‑world open‑source contributions.

Рабочий процесс

• Join MRM Research and complete task phase
• Study Transformers, GANs, LLMs, and general deep learning concepts
• Conduct independent research on mechanistic interpretability using TDA and Ball Mapper on SAE feature spaces
• Read "Hands‑On Machine Learning with Scikit‑Learn, and PyTorch" to strengthen fundamentals
• Improve programming skills and understand open‑source workflows
• Identify potential GSoC projects and organizations
• Document progress publicly in a learning journal

Доказательства

I've successfully completed my task phase at MRM Research (AI) and am now officially part of the team!

Источник

I'm investigating the use of Ball Mapper to analyse Sparse Autoencoder (SAE) feature activation spaces.

Источник

I'm spending time improving my programming skills, understanding open-source development workflows, exploring potential organizations and projects, and learning how to make meaningful contributions to existing codebases.

Источник