Euisuk's Dev Log

ใ€Œ์„œ์ฟ  ๊ฐœ๋ฐœ๋…ธํŠธโญใ€

[๊ฐœ๋…] GLU์™€ ๊ทธ ๋ณ€ํ˜•๋“ค: ์—ญ์‚ฌ์™€ ์ฃผ์š” ๊ฐœ๋… ์ •๋ฆฌ

[๊ฐœ๋…] GLU์™€ ๊ทธ ๋ณ€ํ˜•๋“ค: ์—ญ์‚ฌ์™€ ์ฃผ์š” ๊ฐœ๋… ์ •๋ฆฌ ๋”ฅ๋Ÿฌ๋‹ ๋ถ„์•ผ์—์„œ ํ™œ์„ฑํ™” ํ•จ์ˆ˜๋Š” ์‹ ๊ฒฝ๋ง ๋ชจ๋ธ์ด ํ•™์Šตํ•  ๋•Œ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•ฉ๋‹ˆ๋‹ค. ์ด ์ค‘์—์„œ๋„ GLU(Gated Linear Unit)๋Š” ๊ณ ๊ธ‰ ๋ชจ๋ธ์—์„œ ์ž์ฃผ ์‚ฌ์šฉ๋˜๋Š” ํ™œ์„ฑํ™” ํ•จ์ˆ˜ ์ค‘ ํ•˜๋‚˜์ž…๋‹ˆ๋‹ค. ํŠนํžˆ ์ž์—ฐ์–ด ์ฒ˜๋ฆฌ(NLP)์™€ ๊ฐ™์€ ๋ถ„์•ผ์—์„œ ๋›ฐ์–ด๋‚œ ์„ฑ๋Šฅ์„ ๋ฐœํœ˜ํ•˜๋ฉฐ, ์ด ํ™œ์„ฑํ™” ํ•จ์ˆ˜์—์„œ ํŒŒ์ƒ๋œ ์—ฌ๋Ÿฌ ๋ณ€ํ˜•...

[๊ฟ€ํŒ] .bashrc๋กœ ๋กœ์ปฌ์—์„œ ALIAS๋ฅผ ํ™œ์šฉํ•œ ์—ฌ๋Ÿฌ ์ฟ ๋‹ค ํ™œ์šฉํ•˜๊ธฐ

[๊ฟ€ํŒ] .bashrc๋กœ ๋กœ์ปฌ์—์„œ ALIAS๋ฅผ ํ™œ์šฉํ•œ ์—ฌ๋Ÿฌ ์ฟ ๋‹ค ํ™œ์šฉํ•˜๊ธฐ Introduction ์—ฌ๋Ÿฌ ๋ช…์ด ํ•จ๊ป˜ ์‚ฌ์šฉํ•˜๋Š” ์„œ๋ฒ„ ํ™˜๊ฒฝ์—์„œ CUDA ๋ฒ„์ „์„ ๊ด€๋ฆฌํ•˜๋Š” ๊ฒƒ์€ ๊นŒ๋‹ค๋กœ์šด ์ผ์ž…๋‹ˆ๋‹ค. ์‹œ์Šคํ…œ ์ „์—ญ์— ์˜ํ–ฅ์„ ์ฃผ์ง€ ์•Š๊ณ , ๋ณธ์ธ์˜ ๊ฐœ๋ฐœ ํ™˜๊ฒฝ์—์„œ๋งŒ CUDA ๋ฒ„์ „์„ ๋ฐ”๊พธ์–ด ์‚ฌ์šฉํ•˜๊ณ  ์‹ถ๋‹ค๋ฉด ์–ด๋–ป๊ฒŒ ํ•ด์•ผ ํ• ๊นŒ์š”? ํŠนํžˆ, ์„œ๋กœ ๋‹ค๋ฅธ ํ”„๋กœ์ ํŠธ์—์„œ ๊ฐ๊ฐ ๋‹ค๋ฅธ ...

[๊ฐ•์˜๋…ธํŠธ] LangChain Academy : Introduction to LangGraph (Module 1)

[๊ฐ•์˜๋…ธํŠธ] LangChain Academy : Introduction to LangGraph (Module 1) ๋žญ์ฒด์ธ(LangChain)๊ณผ ๋žญ๊ทธ๋ž˜ํ”„(LangGraph)๋Š” ๋Œ€๊ทœ๋ชจ ์–ธ์–ด ๋ชจ๋ธ(LLM)์„ ํ™œ์šฉํ•œ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜ ๊ฐœ๋ฐœ์„ ์œ„ํ•œ ๋„๊ตฌ๋“ค์ž…๋‹ˆ๋‹ค. ์œ„ ๊ฐ•์˜๋Š” LangChain์—์„œ ์šด์˜ํ•˜๋Š” LangChain Academy์—์„œ ์ œ์ž‘ํ•œ โ€œIntrodu...

[Paper Review] NLP ๊ณต๋ถ€ํ•˜๋Š” ์‚ฌ๋žŒ์ด๋ผ๋ฉด ๊ผญ ์ฝ์–ด์•ผํ•˜๋Š” ๋…ผ๋ฌธ ๋Œ€์‹  ์ •๋ฆฌํ•ด๋“œ๋ฆฝ๋‹ˆ๋‹ค

[Paper Review] NLP ๊ณต๋ถ€ํ•˜๋Š” ์‚ฌ๋žŒ์ด๋ผ๋ฉด ๊ผญ ์ฝ์–ด์•ผํ•˜๋Š” ๋…ผ๋ฌธ ๋Œ€์‹  ์ •๋ฆฌํ•ด๋“œ๋ฆฝ๋‹ˆ๋‹ค โœ๏ธ ์ž์—ฐ์–ด ์ฒ˜๋ฆฌ(NLP)๋Š” ๋น ๋ฅด๊ฒŒ ๋ฐœ์ „ํ•˜๊ณ  ์žˆ๋Š” ๋ถ„์•ผ๋กœ, ์ˆ˜๋งŽ์€ ํš๊ธฐ์ ์ธ ์—ฐ๊ตฌ ๋…ผ๋ฌธ๋“ค์ด ๋งค๋…„ ๋ฐœํ‘œ๋˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ๋งŒ์•ฝ ์—ฌ๋Ÿฌ๋ถ„์ด NLP์— ์ฒ˜์Œ ๋ฐœ์„ ๋“ค์ด๊ฑฐ๋‚˜, ์—ฐ๊ตฌ๋ฅผ ๋” ๊นŠ์ด ์ดํ•ดํ•˜๊ณ ์ž ํ•œ๋‹ค๋ฉด, ๋‹ค์Œ์— ์†Œ๊ฐœํ•  ๋…ผ๋ฌธ๋“ค์ด ํ•ต์‹ฌ ๊ฐœ๋…๊ณผ ์ตœ๊ทผ ๋ฐœ์ „ ๋™ํ–ฅ์„ ํŒŒ์•…...

[ํŠธ๋ Œ๋“œ] 2025๋…„ ํŠธ๋ Œ๋“œ : LMM, LAM, AGENT, ๊ทธ๋ฆฌ๊ณ  FMOps

[ํŠธ๋ Œ๋“œ] 2025๋…„ ํŠธ๋ Œ๋“œ : LMM, LAM, AGENT, ๊ทธ๋ฆฌ๊ณ  FMOps Introduction 2023๋…„ ChatGPT๋กœ ์ธํ•ด LLM(Large Language Model)์˜ ์‹œ๋Œ€๊ฐ€ ์—ด๋ฆฌ๋ฉด์„œ, 2024๋…„์€ LLM์ด ๋ณด๋‹ค ๊ณ ๋„ํ™”๋˜๋ฉด์„œ RAG, LLMOps ๋“ฑ ๋งŽ์€ ์‹œ์žฅ ๋ฐ AI์—ฐ๊ตฌ์— ๋ณ€ํ™”๋ฅผ ์ฃผ๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ๋งˆ์ฐฌ๊ฐ€์ง€๋กœ, 2025๋…„์ด ๋‹ค๊ฐ€์˜ค๋ฉด...

[Paper Review] Mamba2 - Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

[Paper Review] Mamba2 - Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality ๋งํฌ : https://arxiv.org/pdf/2405.21060 ๋‹ค์Œ์€ ๋…ผ๋ฌธ โ€œTransformers ...

[Paper Review] A COMPREHENSIVE REVIEW OF YOLO ARCHITECTURES IN COMPUTER VISION: FROM YOLOV1 TO YOLOV8 AND YOLO-NAS

[Paper Review] A COMPREHENSIVE REVIEW OF YOLO ARCHITECTURES IN COMPUTER VISION: FROM YOLOV1 TO YOLOV8 AND YOLO-NAS YOLO ๋ชจ๋ธ ์„œ๋ฒ ์ด ํŽ˜์ดํผ ๋งํฌ : https://www.mdpi.com/2504-4990/5/4/83 ๐Ÿ“– (์ฐธ๊ณ ) YOLO ๋ฒ„...

[Paper Review] Mamba: Linear-Time Sequence Modeling with Selective State Spaces

[Paper Review] Mamba: Linear-Time Sequence Modeling with Selective State Spaces ์ตœ๊ทผ ๋”ฅ๋Ÿฌ๋‹ ์•„ํ‚คํ…์ฒ˜์˜ ์ค‘์‹ฌ์—๋Š” ํŠธ๋žœ์Šคํฌ๋จธ๊ฐ€ ์ž๋ฆฌ ์žก๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ๋Œ€๊ทœ๋ชจ ์–ธ์–ด ๋ชจ๋ธ(LLM)๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, ๊ทธ๋ฆผ์„ ์ƒ์„ฑํ•˜๋Š” ๋ฐ ์“ฐ์ด๋Š” ๋””ํ“จ์ „ ๋ชจ๋ธ ๋˜ํ•œ ํŠธ๋žœ์Šคํฌ๋จธ ๊ตฌ์กฐ๋ฅผ ํ™œ์šฉํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์ด์™ธ์—๋„ ์‹œ๊ณ„์—ด ๋ถ„์„...

[Paper Review] Structured State Space Models for Deep Sequence Modeling

[Paper Review] Structured State Space Models for Deep Sequence Modeling ์‹œ๊ณ„์—ด ๋ฐ์ดํ„ฐ๋ฅผ ํšจ์œจ์ ์œผ๋กœ ์ฒ˜๋ฆฌํ•˜๋Š” ๋ฐฉ๋ฒ•์€ ์ง€๋‚œ ๋ช‡ ๋…„ ๋™์•ˆ ๋น ๋ฅด๊ฒŒ ๋ฐœ์ „ํ–ˆ์Šต๋‹ˆ๋‹ค. ํŠนํžˆ, CMU์— ๊ณ„์‹  Albert Gu ๊ต์ˆ˜๋‹˜์€ ๊ธด ์‹œ๊ณ„์—ด ์˜์กด์„ฑ(Long-Range Dependencies, LRDs)์„ ์ฒ˜๋ฆฌํ•˜๋Š” ๋ฐ ์ง‘์ค‘...

[๊ฐœ๋…] ์ธ๊ณต์ง€๋Šฅ์„ ์œ„ํ•œ ์„ ํ˜•๋Œ€์ˆ˜ : ํ–‰๋ ฌํŽธ

[๊ฐœ๋…] ์ธ๊ณต์ง€๋Šฅ์„ ์œ„ํ•œ ์„ ํ˜•๋Œ€์ˆ˜ : ํ–‰๋ ฌํŽธ ํ–‰๋ ฌ(Matrix)์˜ ์ •์˜ ์ธ๊ณต์ง€๋Šฅ ๋ฐ ๊ธฐ๊ณ„ ํ•™์Šต์—์„œ ํ–‰๋ ฌ(matrix)์€ ๋ฐ์ดํ„ฐ๋ฅผ ์ฒ˜๋ฆฌํ•˜๊ณ  ํ‘œํ˜„ํ•˜๋Š” ์ค‘์š”ํ•œ ๋„๊ตฌ์ž…๋‹ˆ๋‹ค. ํ–‰๋ ฌ์€ ์ˆ˜๋ฅผ ์ง์‚ฌ๊ฐํ˜• ํ˜•ํƒœ๋กœ ๋ฐฐ์—ดํ•œ ๊ฒƒ์œผ๋กœ, ๋ฐ์ดํ„ฐ๋ฅผ ์ˆ˜ํ•™์ ์œผ๋กœ ๋‹ค๋ฃจ๊ธฐ ์œ„ํ•ด ํ•„์ˆ˜์ ์ž…๋‹ˆ๋‹ค. ํ–‰๋ ฌ์„ ์‚ฌ์šฉํ•˜๋ฉด ๋Œ€๊ทœ๋ชจ ๋ฐ์ดํ„ฐ๋ฅผ ํšจ์œจ์ ์œผ๋กœ ๊ด€๋ฆฌํ•˜๊ณ  ๊ณ„์‚ฐํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค...