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

说明与用途

一名计算机科学学生描述了在AI、深度学习和开源领域的学习与研究计划，包括在MRM Research的工作、基于TDA的可解释性研究以及GSoC的准备。

问题

A second‑year CS student seeks to structure and document a learning path in AI, deep learning, research, and open source contributions.

结果

The student has established a structured learning and contribution plan, integrating research, skill development, and public documentation.

工作流

• Join MRM Research and complete task phase
• Study Transformers, GANs, LLMs, and general deep learning concepts
• Conduct research on mechanistic interpretability using TDA and Ball Mapper on SAE feature spaces
• Prepare for GSoC by improving programming skills, learning open‑source workflows, and identifying projects
• Read and implement algorithms from Hands‑On Machine Learning with Scikit‑Learn and PyTorch
• Publish regular posts on concepts, research, experiments, GSoC progress, and lessons learned

证据

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

来源

My current research interest lies in Mechanistic Interpretability, particularly exploring how Topological Data Analysis (TDA) can help us understand neural network representations.

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

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Another goal I'm actively working towards is Google Summer of Code (GSoC).

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I'm also currently reading Hands‑On Machine Learning with Scikit‑Learn, and PyTorch.

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