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

Description and use

A second‑year CS student has completed a research task at MRM Research, is conducting independent research on mechanistic interpretability using TDA, is strengthening fundamentals, and is preparing for Google Summer of Code while documenting the journey publicly.

Problem

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

Outcome

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

Workflow

• 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

Evidence

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

Source

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

Source

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.

Source