📚 Resources

Study notes, guides, and tutorials I’ve compiled while learning about ML, NLP, GPU optimization, and signal processing.

Study Notes

• CS336: LLM from Scratch (Stanford SP25) — Notes from Stanford’s course on building LLMs from the ground up.
• DUL: Deep Unsupervised Learning (Berkeley SP24) — Lecture notes from Berkeley’s DUL course covering LLMs, scaling laws, and architectures.
• GPU & LLM Inference — Optimization techniques for LLM inference, KV caching, batching strategies, and PagedAttention.
• Natural Language Processing — Notes on Transformers, attention mechanisms, and hybrid architectures.
• Signal Processing — Study notes on spectrograms, time-frequency analysis, and audio signal classification.