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2025

[๊ฟ€ํŒ] ๋ฏธ๋“œ์ €๋‹ˆ(Midjourney) ๊ธฐ์ดˆ ๊ฐ€์ด๋“œ


[๊ฐœ๋…] API์— ๋Œ€ํ•ด์„œ ์ดํ•ดํ•ด๋ณด์ž!


๋‚ด๊ฐ€ ๋ณด๋ ค๊ณ  ์ •๋ฆฌํ•œ ํ„ฐ๋ฏธ๋„ ํ•จ์ˆ˜ 25์„ 


๐Ÿ’ป ๋‚ด๊ฐ€ ๋ณด๋ ค๊ณ  ์ž‘์„ฑํ•œ UV ์™„๋ฒฝ ๊ฐ€์ด๋“œ


[๊ฟ€ํŒ] ์‚ฌ๋žŒ์ฒ˜๋Ÿผ ๊ธ€์„ ์“ฐ๋Š” ํ”„๋กฌํ”„ํŒ… ํŒ


OpenAI, o3 & o4-mini ๊ณต๊ฐœ: AI ์ถ”๋ก ์˜ ์ง„ํ™”


[๊ฟ€ํŒ] ์›น์‚ฌ์ดํŠธ๋ฅผ ์•ฑ์ฒ˜๋Ÿผ! + Win ๋‹จ์ถ•ํ‚ค๋กœ ์ดˆ๊ฐ„ํŽธ ์‹คํ–‰ํ•˜๋Š” ๊ฟ€ํŒ (๐Ÿ’ป + ๐Ÿ”ข)


OpenAI, ๊ฐœ๋ฐœ์ž์šฉ API GPT 4.1 ๊ณต๊ฐœ


[๊ฟ€ํŒ] Sora AI ์†Œ๊ฐœ ๋ฐ Presets ๊ธฐ๋Šฅ ํ™œ์šฉ ๊ฐ€์ด๋“œ


Google NotebookLM: ๋‚˜๋งŒ์˜ AI ์ง€์‹ ๋น„์„œ


[๊ฐœ๋…] ๋ชจ๋ธ ์ปจํ…์ŠคํŠธ ํ”„๋กœํ† ์ฝœ(MCP) ์™„์ „ ์ •๋ณต


OpenAI, GPTโ€‘4o ์ด๋ฏธ์ง€ ์ƒ์„ฑ ๊ธฐ๋Šฅ ์†Œ๊ฐœ โ€“ AI ์ด๋ฏธ์ง€ ์ƒ์„ฑ์˜ ์ƒˆ๋กœ์šด ์‹œ๋Œ€


OpenAI, Audio Models in the API ์ถœ์‹œ


[์ธํ„ฐ๋ทฐ] NVIDIA CEO ์  ์Šจํ™ฉ์ด ๋งํ•˜๋Š” AI์˜ ๋ฏธ๋ž˜


OpenAI, ์ƒˆ๋กœ์šด ์—์ด์ „ํŠธ ๊ฐœ๋ฐœ ๋„๊ตฌ ๋ฐœํ‘œ


[๊ฐœ๋…์ •๋ฆฌ] Streamlit๐Ÿ‘‘ ์†Œ๊ฐœ ๋ฐ ํ™œ์šฉ ๊ฐ€์ด๋“œ


[๊ฐœ๋…์ •๋ฆฌ] ๋ฉ”ํƒ€-๋ถ„์„๊ณผ ์„œ๋ฒ ์ด ํŽ˜์ดํผ


[๋จธ์‹ ๋Ÿฌ๋‹] ๋กœ์ง€์Šคํ‹ฑ ํšŒ๊ท€ ๋ชจ๋ธ (Logistic Regression)


[๊ฟ€ํŒ] VS Code ํŠน์ • ๋ฒ„์ „ ์„ค์น˜ ๋ฐ ๋‹ค์šด๊ทธ๋ ˆ์ด๋“œ ๋ฐฉ๋ฒ•


[Pandas] ํŒ๋‹ค์Šค ํ”ผํด(.pkl) ํŒŒ์ผ์˜ ์••์ถ• ๋ฐฉ์‹ ๋น„๊ต


OpenAI, ๋” ์ž์—ฐ์Šค๋Ÿฝ๊ณ  ์ง€๋Šฅ์ ์ธ ๋Œ€ํ™” ๋Šฅ๋ ฅ์„ ๊ฐ–์ถ˜ GPT-4.5 ์ถœ์‹œ


OpenAI, Deep Research ๊ธฐ๋Šฅ ๊ณต๊ฐœ


(์„ค๋ช…์ถ”๊ฐ€) ์›จ์ดํŠธ ์ดˆ๊ธฐํ™” (Weight Initialization)


OpenAI o3-mini: ์ƒˆ๋กœ์šด ๋น„์šฉ ํšจ์œจ์  AI ๋ชจ๋ธ ์ถœ์‹œ


(์„ค๋ช…์ถ”๊ฐ€) Q-Learning: ๊ฐ•ํ™”ํ•™์Šต์˜ ํ•ต์‹ฌ ๊ฐœ๋…๊ณผ ์ดํ•ด


[๊ฟ€ํŒ] GPT Canvas์—์„œ LaTeX ์ˆ˜์‹ ๋žœ๋”๋ง ๋ฌธ์ œ ๋ฐ ํ•ด๊ฒฐ ๋ฐฉ๋ฒ•


[NLP] 5. ์ž์—ฐ์–ด ์ฐจ์› ์ถ•์†Œ(Dimension Reduction) ๊ธฐ๋ฒ•


[NLP] 6. Topic Modeling์ด๋ž€?


(์„ค๋ช…์ถ”๊ฐ€) Perplexity์™€ BLEU ์Šค์ฝ”์–ด์— ๋Œ€ํ•œ ๋ณด์ถฉ ์„ค๋ช…


[๋„์„œ๋ฆฌ๋ทฐ] ๋”ฅ๋Ÿฌ๋‹ ์ž…๋ฌธ์ž๋ฅผ ์œ„ํ•œ ์ฑ… ์ถ”์ฒœ, ํ˜ํŽœํ•˜์ž„ ใ€ŽEasy! ๋”ฅ๋Ÿฌ๋‹ใ€


[OpenAI] Introduction to Operator & Agents : Computer-Using Agent


[๊ฟ€ํŒ] ์œ ๋‹ˆ์ฝ”๋“œ ๋ฌธ์žํ‘œ ๋ชจ์Œ์ง‘ ์‚ฌ์ดํŠธ ์†Œ๊ฐœ!!


ChatGPT Tasks: ํ˜์‹ ์ ์ธ ์ž‘์—… ๊ด€๋ฆฌ ๋„๊ตฌ


[CES 2025] Keynote : Accenture Chair and CEO, Julie Sweet


[CES 2025] Keynote : CEO of Delta, Ed Bastian


[CES 2025] Keynote : CEO of X Corp, Linda Yaccarino


[CES 2025] Keynote : NVIDIA Founder and CEO, Jensen Huang


[CES 2025] Keynote : Panasonic Holdings CEO , Mr. Yuki Kusumi


[CES 2025] Keynote : SiriusXM CEO, Jennifer Witz


[CES 2025] Keynote : Volvo Group President and CEO, Martin Lundstedt


์ œ1ํšŒ AI FC (AI Fight Club) ๋Œ€ํšŒ ์†Œ๊ฐœ ๋ฐ ์ •๋ณด ๊ณต์œ ๐ŸฅŠ


2024

Veo2: ์ฐจ์„ธ๋Œ€ AI ๋น„๋””์˜ค ์ƒ์„ฑ ๋ชจ๋ธ (Veo2 vs Sora)


[TREND] CES 2025 ๋ฏธ๋ฆฌ๋ณด๊ธฐ : CES 2025์—์„œ ์ฃผ๋ชฉํ•ด์•ผ ํ•  ํŠธ๋ Œ๋“œ ์‚ดํŽด๋ณด์ž!


[์ •๋ฆฌ] '24๋…„ AI Summit : '๋ผ๋งˆ' ๊ฐœ๋ฐœ ๋ฆฌ๋”๊ฐ€ ์„ค๋ช…ํ•˜๋Š” LLM : Small Models ์ตœ์‹  ๊ธฐ๋ฒ• - Soumya Batra


[์ •๋ฆฌ] '24๋…„ AI Summit : ์‰ฝ๊ฒŒ ์ดํ•ดํ•˜๋Š” RAG์™€ ์—์ด์ „ํŠธ AI ๊ธฐ์ˆ ์˜ ํ˜„์žฌ์™€ ๋ฏธ๋ž˜ - ์ตœ์œค์„


[ํŒŒ์ด์ฌ] ๋ฐ์ฝ”๋ ˆ์ดํ„ฐ(Decorator) ์‚ฌ์šฉ๋ฒ•


[TREND] Google Cloud 2025 AI business trend


[Day 11] ChatGPT x Desktop Application ๐Ÿ’ป


[Day 12] introducing o3 and o3-mini


[Day 10] 1-800-CHAT-GPT: ChatGPT๋ž‘ ํ†ตํ™”๋ž‘ ๋ฌธ์ž๋ฅผ ํ•  ์ˆ˜ ์žˆ๋‹ค๊ณ ? ๐Ÿ“ž๐Ÿ’ฌ


[Day 9] OpenAI o1 ๋ฐ ๊ฐœ๋ฐœ์ž๋ฅผ ์œ„ํ•œ ์ƒˆ๋กœ์šด ๋„๊ตฌ


[Day 8] Introducing ChatGPT Search


[Day 7] Introducing Projects


[๊ฟ€ํŒ] Cursor AI - AI ๊ธฐ๋ฐ˜ ์ฝ”๋“œ ํŽธ์ง‘๊ธฐ


[Day 6] Santa Mode & Video in Advanced Voice


[Day 5] ChatGPT x Apple Intelligence


[Day 4] Canvas ๊ธฐ๋Šฅ, ์ „๋ณด๋‹ค ํ™•์‹คํžˆ ๋” ์ข‹์•„์ง„๋“ฏ?!


[Day 3] SORA ๋–ด๋‹ค!! ํ˜์‹ ์ ์ธ AI ๋น„๋””์˜ค ์ƒ์„ฑ ํ”Œ๋žซํผ์˜ ๋“ฑ์žฅ


[Day 2] Reinforcement Fine-Tuning (RFT) ์†Œ๊ฐœ


[Day 1] OpenAI o1 and o1 pro mode in ChatGPT


[TREND] ํŠธ๋ Œ์Šคํฌ๋จธ ์ดํ›„์˜ ์ฐจ์„ธ๋Œ€ ์•„ํ‚คํ…์ณ: MoE, SSM, RetNet, V-JEPA


[์ž๋ฃŒ] KT ์‚ฌ๋‚ด ์ง์› ๋Œ€์ƒ RAG ๊ต์•ˆ ๋Œ€๋ฐฉ์ถœ?!


[IT] ํด๋ผ์šฐ๋“œ ์ปดํ“จํŒ…์˜ ์„œ๋น„์Šค ๋ชจ๋ธ: IaaS, PaaS, SaaS์™€ ์ตœ์‹  GPUaaS, IQaaS ๋น„๊ต


[์ •๋ฆฌ] SK AI SUMMIT ํ‚ค๋…ธํŠธ ์ •๋ฆฌ - Day2


[์ •๋ฆฌ] SK AI SUMMIT ํ‚ค๋…ธํŠธ ์ •๋ฆฌ - Day1 ์˜คํ›„


[์ •๋ฆฌ] SK AI SUMMIT ํ‚ค๋…ธํŠธ ์ •๋ฆฌ - Day1 ์˜ค์ „


[Linux] ๋ฆฌ๋ˆ…์Šค ํŒŒ์ผ ์‹œ์Šคํ…œ ๋งˆ์šดํŠธ ๊ฐ€์ด๋“œ


[๊ฐ•์˜๋…ธํŠธ] LangChain Academy : Introduction to LangGraph (Module 2)


[๊ฐ•์˜๋…ธํŠธ] LangChain Academy : Introduction to LangGraph (Module 3)


[๊ฐ•์˜๋…ธํŠธ] LangChain Academy : Introduction to LangGraph (Module 4)


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


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


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


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


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


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


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


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


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


[Paper Review] Resurrecting Recurrent Neural Networks for Long Sequences


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


[๊ฐœ๋…] ์‹œ์Šคํ…œ, ๋ฏธ๋ถ„๋ฐฉ์ •์‹, ๊ทธ๋ฆฌ๊ณ  ์ƒํƒœ ๊ณต๊ฐ„ ๋ชจ๋ธ


[IT] LLMOps์™€ RAG


[์ •๋ฆฌ] OpenAI API Document


[๊ฐ•์˜๋…ธํŠธ] Text Splitting For Retrieval


[๊ฐ•์˜๋…ธํŠธ] RAG From Scratch : Coursework


[๊ฐ•์˜๋…ธํŠธ] RAG From Scratch : Overview


[๊ฐ•์˜๋…ธํŠธ] RAG From Scratch : Query Indexing ๊ธฐ๋ฒ•


[๊ฐ•์˜๋…ธํŠธ] RAG From Scratch : Query Retrieval ๊ธฐ๋ฒ•


[๊ฐ•์˜๋…ธํŠธ] RAG From Scratch : Query Routing & Structuring


[๊ฐ•์˜๋…ธํŠธ] RAG From Scratch : Query Translation


[๊ฐ•์˜๋…ธํŠธ] RAG From Scratch : RAG for long context LLMs


[GPU] nvidia-smi์˜ ์‹œ๋Œ€๋Š” ๊ฐ”๋‹ค?


[OpenAI] GPT-4o ๋‹ค์Œ ๋ฒ„์ „ ๋–ด๋‚˜!?


[ํŒŒ์ด์ฌ] ์ •๊ทœํ‘œํ˜„์‹ ํ™œ์šฉ ๋ฐฉ๋ฒ•


[Pandas] ์‹ค๋ฌด์—์„œ ์“ฐ๋ ค๊ณ  ์ •๋ฆฌํ•ด๋‘” Code Snippet


[๊ฐœ๋…] ๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜ ์˜์‚ฌ๊ฒฐ์ • ๋ฐฉ๋ฒ•


[Paper Review] ํ•ฉ์„ฑ ๋ฐ์ดํ„ฐ = ๋ชจ๋ธ ๋ถ•๊ดด?


[๊ฟ€ํŒ] ํ”„๋กฌํ”„ํŠธ ์—”์ง€๋‹ˆ์–ด๋ง (๊ฐ•์˜ ์š”์•ฝ)


[CV Notes] Lecture 18 - Videos


[CS294] Deep Unsupervised Learning: Introduction


[CV Notes] Lecture 17 - 3D Vision


[NLP] 3. Natural Language Preprocessing


[NLP] 4. Natural Language Embeddings


[NLP] 1. Introduction to Text Analytics


[NLP] 2. Steps of Text Analytics


[๊ธฐ์—…] NVIDIA์™€ GPU ์ด์•ผ๊ธฐ


[Graph] 3์žฅ. Graph Node Embedding Methods


[Graph] 4์žฅ. Graph Neural Networks: Algorithms


[Graph] 1์žฅ. ๊ทธ๋ž˜ํ”„์™€ GNN


[Graph] 2์žฅ. Graph Neural Networks


Prophet์„ ํ™œ์šฉํ•œ ์‹œ๊ณ„์—ด ์˜ˆ์ธก๐Ÿ”ญ


[๊ฐœ๋…์ •๋ฆฌ] ์ƒ๊ด€๊ด€๊ณ„ vs ์ธ๊ณผ๊ด€๊ณ„


[๊ฐœ๋…์ •๋ฆฌ] ์‹œ๊ณ„์—ด ์ธ๊ณผ๊ด€๊ณ„ ๋ถ„์„: Granger Causality


[๊ฐœ๋…์ •๋ฆฌ] ๋นˆ๋„์ฃผ์˜(Frequentist) vs ๋ฒ ์ด์ง€์•ˆ(Bayesian)


[๊ฐœ๋…] Deep Learning Normalization Techniques


[ํŠธ๋ฆฌ] ํŠธ๋ฆฌ ๊ธฐ๋ฐ˜ ML ์•Œ๊ณ ๋ฆฌ์ฆ˜


[ํŒŒ์ด์ฌ] VS-Code keymap ์˜ค๋ฅ˜ ํ•ด๊ฒฐํ•˜๊ธฐ (shift+enter, ctrl+a ๋“ฑ)


[ํŒŒ์ด์ฌ] ์šฐ๋ถ„ํˆฌ์—์„œ ํ•œ๊ธ€ ํฐํŠธ ์„ค์น˜ํ•˜๊ณ  matplotlib์— ์‚ฌ์šฉํ•˜๊ธฐ


[๊ฐœ๋…] ์‹ ํ˜ธ์ฒ˜๋ฆฌ ๋ฐ ํ‘ธ๋ฆฌ์— ๋ณ€ํ™˜


[๊ฟ€ํŒ] Confluence ๋‹จ์ถ•ํ‚ค๐Ÿ“– (๋ถ๋งˆํฌ ์ถ”์ฒœ!!)


[๊ฟ€ํŒ] Notion ๋‹จ์ถ•ํ‚ค๐Ÿ“– (๋ถ๋งˆํฌ ์ถ”์ฒœ!!)


[Git] Credential Helper๋กœ GitHub ์ธ์ฆํ•˜๊ธฐ ๐Ÿ”‘


[Git] SSH๋กœ GitHub Repository Cloneํ•˜๊ธฐ ๐Ÿ”‘


[๊ฟ€ํŒ] Python์œผ๋กœ ๋‹ค์–‘ํ•œ ๋ฐ์ดํ„ฐ ๋‹ค๋ฃจ๊ธฐ


[์ดํƒ] ๊ฑฐ๋ฆฌ ๊ธฐ๋ฐ˜ ์ด์ƒํƒ์ง€ ์•Œ๊ณ ๋ฆฌ์ฆ˜


[์ดํƒ] ๊ฒฐ์ • ๊ฒฝ๊ณ„ ๊ธฐ๋ฐ˜ ์ด์ƒํƒ์ง€ ์•Œ๊ณ ๋ฆฌ์ฆ˜


[์ดํƒ] ๋‚ด๊ฐ€ ๋ณด๋ ค๊ณ  ์ •๋ฆฌํ•œ ML ์ด์ƒํƒ์ง€


[์ดํƒ] ๋ฐ€๋„ ๊ธฐ๋ฐ˜ ์ด์ƒํƒ์ง€ ์•Œ๊ณ ๋ฆฌ์ฆ˜


[์ดํƒ] ์•™์ƒ๋ธ” ๊ธฐ๋ฐ˜ ์ด์ƒํƒ์ง€ ์•Œ๊ณ ๋ฆฌ์ฆ˜


[์ดํƒ] ํ†ต๊ณ„ ๊ธฐ๋ฐ˜ ์ด์ƒํƒ์ง€ ์•Œ๊ณ ๋ฆฌ์ฆ˜


[๊ฟ€ํŒ] ์„ค๋งˆ ์ˆ˜์‹/ํ…Œ์ด๋ธ” ์ง์ ‘ ์ฝ”๋“œ๋กœ ์ž‘์„ฑํ•˜์‹œ๋‚˜์š”?!


[์„ค์น˜] pipenv๋กœ pyenv ๋ฝ• ๋ฝ‘๊ธฐ


[์„ค์น˜] pyenv ์–ด์„œ ์˜ค๊ณ 


[Linux] ๋”ฅ๋Ÿฌ๋‹ ํ™˜๊ฒฝ ๊ตฌ์ถ• : CUDA, CuDNN


[๊ฟ€ํŒ] ์ง์žฅ์ธ ํ•„์ˆ˜! ํŒŒ์›Œํฌ์ธํŠธ ๋‹จ์ถ•ํ‚ค ๋ชจ์Œ TOP 10


๋„คํŠธ์›Œํฌ ๊ณ„์ธต : OSI 7๊ณ„์ธต vs TCP/IP 5๊ณ„์ธต


๋ฆฌ๋ˆ…์Šค : ๋ฆฌ๋ˆ…์Šค ๊ตฌ์„ฑ ์š”์†Œ


๋„คํŠธ์›Œํฌ ๊ธฐ์ดˆ : ๋„คํŠธ์›Œํฌ ๊ทธ๋ฆฌ๊ณ  ์„œ๋ฒ„๋ž€?


[์‚ฌ์ดํŠธ] ์ด๋ชจํ‹ฐ์ฝ˜๐Ÿค—์œผ๋กœ ๊ธ€์— ์ƒ๋ช… ๋ถˆ์–ด๋„ฃ๊ธฐ : EmojiCombos


[์‚ฌ์ดํŠธ] ์ง์žฅ์ธ์„ ์œ„ํ•œ ๋ฌด๋ฃŒ ํ”ฝํ† ๊ทธ๋žจ ์‚ฌ์ดํŠธ TOP 2


[๊ฐœ๋…] ์ƒ์„ฑ AI์˜ ํ•™์Šต ๋ฐฉ์‹: ์ œ๋กœ์ƒทยท์›์ƒทยทํ“จ์ƒท ๋Ÿฌ๋‹


[Conda] Miniforge๋ฅผ ์ด์šฉํ•œ ๋ถ„์„ ํ™˜๊ฒฝ ์„ค์ •ํ•˜๊ธฐ (OS: Ubuntu)


[Conda] ํ™œ์šฉ ๊ฐ€์ด๋“œ: ํšจ์œจ์ ์ธ ํ™˜๊ฒฝ ๊ด€๋ฆฌ์™€ Jupyter Kernel ์„ค์ •


[Git] Git์„ ์–ด๋–ป๊ฒŒ ํ•ฉ์น˜์ง€? : Git Merging


[IT] ๊ฐœ๋ฐœ์ž๋“ค์˜ ์ข…๋ฅ˜ ๋ฐ ์—ญํ• 


[์ปจํผ][CIO] Gen AI ์‹œ๋Œ€๋ฅผ ๋Œ€๋น„ํ•˜๋Š” ์ „๋žต, โ€˜์—”ํ„ฐํ”„๋ผ์ด์ฆˆ AI ํ”Œ๋žซํผโ€™ ๋„์ž… ๊ฐ€์ด๋“œ


[์ปจํผ][CIO] ์Šค์Šค๋กœ ํƒ๊ตฌํ•˜๋Š” AGI์™€ GPU ์ธํ”„๋ผ ์ด์•ผ๊ธฐ


[์ปจํผ][CIO] ์ƒ์„ฑํ˜• AI์™€ Low Code๋ฅผ ํ™œ์šฉํ•œ ๋ฐ์ดํ„ฐ ๋ถ„์„ ์—…๋ฌด ์ƒ์‚ฐ์„ฑ ๊ทน๋Œ€ํ™” ์ „๋žต


[์ปจํผ][CIO] GenAI, First!


[์ปจํผ][CIO] Workday AI/ML ์†”๋ฃจ์…˜๊ณผ Generative AI์˜ ๋ฏธ๋ž˜


[์ปจํผ][CIO] '24๋…„ CIO SUMMIT ํ›„๊ธฐ


[์ปจํผ][CIO] '24๋…„ ๋น„์ฆˆ๋‹ˆ์Šค ํ˜์‹ ์„ ๊ฐ€์†ํ™”ํ•˜๋Š” ์ƒ์„ฑํ˜• AI


[ํŒŒ์ด์ฌ] Multiprocessing, Multithreading ์‚ฌ์šฉ ์‹œ ๊ณ ๋ ค ์‚ฌํ•ญ


[ํŒŒ์ด์ฌ] Multiprocessing, Multithreading ์ž์› ๋ถ„ํ•  ๋ฐ ํ• ๋‹น


[ํŒŒ์ด์ฌ] multiprocessing์˜ Pool ํด๋ž˜์Šค


[ํŒŒ์ด์ฌ] ์‰ฝ๊ฒŒ ์„ค๋ช…ํ•œ Process vs Thread


[Linux] ํŒŒ์ผ ๊ถŒํ•œ๊ณผ ์“ฐ๊ธฐ ์ž‘์—…


[Linux] ์‚ฌ์šฉ์ž ๊ณ„์ • ๋ฐ ๊ทธ๋ฃน ๊ด€๋ฆฌ ๊ฐ€์ด๋“œ


[๊ฐœ๋…] MLOps vs AIOps


[Linux] ์—ด๋ ค ์žˆ๋Š” ํŒŒ์ผ ๋ฐ ๋ฆฌ์†Œ์Šค ๊ด€๋ฆฌ์˜ ํ•„์ˆ˜ ๋„๊ตฌ(lsof)


2023

[์ปจํผ][OpenAI] 2023 DevDay New Products: A Deep Dive ์š”์•ฝ


[์ปจํผ][OpenAI] 2023 DevDay Opening KeyNote ์š”์•ฝ


[์ปจํผ][OpenAI] 2023 Techniques for Maximizing LLM Performance ์š”์•ฝ


[TS] ์‹œ๊ณ„์—ด ์ด์ƒํƒ์ง€ ์‹œ๊ฐํ™”


[TS] ์ฃผ/์›”๋ณ„ ์ดˆ๊ธฐ ๋‚ ์งœ ๋ณ€ํ™˜ ํ•จ์ˆ˜


ํŒŒ์ด์ฌ ๋งˆ์Šคํ„ฐํ•˜๊ธฐ : Group By ํ•จ์ˆ˜


ํŒŒ์ด์ฌ ๋งˆ์Šคํ„ฐํ•˜๊ธฐ : apply(), map(), applymap() ํ•จ์ˆ˜


ํŒŒ์ด์ฌ ๋งˆ์Šคํ„ฐํ•˜๊ธฐ : ๋žŒ๋‹ค(Lambda) ํ•จ์ˆ˜


[SPL] Splunk & SPL ๊ฐœ์š”


[SPL] stats์™€ eventstats ์ฐจ์ด์  ์ดํ•ดํ•˜๊ธฐ


ํŒŒ์ด์ฌ ๋งˆ์Šคํ„ฐํ•˜๊ธฐ : Jupyter


ํŒŒ์ด์ฌ ๋งˆ์Šคํ„ฐํ•˜๊ธฐ : Numpy


ํŒŒ์ด์ฌ ๋งˆ์Šคํ„ฐํ•˜๊ธฐ : Pandas


[Git] Git Branch์— ๋Œ€ํ•œ ์ดํ•ด์™€ ํ™œ์šฉ


[Git] Git ์ถฉ๋Œ ์‹œ ํ•ด๊ฒฐ ๋ฐฉ๋ฒ•


[Git] ๊นƒ(Git)์„ ํ™œ์šฉํ•œ ํ”„๋กœ์ ํŠธ ๊ด€๋ฆฌ


[Git] ๊นƒ(Git)์„ ํ™œ์šฉํ•œ ํ˜‘์—… ๋ฐฉ๋ฒ•


[Git] ๊นƒ(Git)์ด๋ž€?


2021

[๋จธ์‹ ๋Ÿฌ๋‹][์ฐจ์›์ถ•์†Œ] ๋ณ€์ˆ˜ ์ถ”์ถœ๋ฒ• - Multi-Dimensional Scaling (MDS)


[ํŒŒ์ดํ† ์น˜] ํŒŒ์ดํ† ์น˜๋กœ CNN ๋ชจ๋ธ์„ ๊ตฌํ˜„ํ•ด๋ณด์ž! (ResNetํŽธ)


[ํŒŒ์ดํ† ์น˜] ํŒŒ์ดํ† ์น˜๋กœ CNN ๋ชจ๋ธ์„ ๊ตฌํ˜„ํ•ด๋ณด์ž! (GoogleNetํŽธ)


[ํŒŒ์ดํ† ์น˜] ํŒŒ์ดํ† ์น˜๋กœ CNN ๋ชจ๋ธ์„ ๊ตฌํ˜„ํ•ด๋ณด์ž! (VGGNetํŽธ)


[ํŒŒ์ดํ† ์น˜] ํŒŒ์ดํ† ์น˜๋กœ CNN ๋ชจ๋ธ์„ ๊ตฌํ˜„ํ•ด๋ณด์ž! (๊ธฐ์ดˆํŽธ + DataLoader ์‚ฌ์šฉ๋ฒ•)


[๋จธ์‹ ๋Ÿฌ๋‹] ๊ฑฐ๋ฆฌโ€ข๊ตฐ์ง‘โ€ข์„œํฌํŠธ๋ฒกํ„ฐ ๊ธฐ๋ฐ˜ ์ด์ƒํƒ์ง€ ๊ธฐ๋ฒ•


[๋จธ์‹ ๋Ÿฌ๋‹] ์ด์ƒ ํƒ์ง€ ๊ฐœ์š” ๋ฐ ๋ฐ€๋„ ๊ธฐ๋ฐ˜ ์ด์ƒ์น˜ํƒ์ง€


[Paper Review] An Image Is Worth 16x16 Words : Transformers for Image Recognition at Scale (Vision Transformer)


[Paper Review] An Architecture Combining Convolutional Neural Network(CNN) and Support Vector Machine(SVM) for Image Classification


[์•Œ๊ณ ๋ฆฌ์ฆ˜] ๊ทธ๋ž˜ํ”„


[๋จธ์‹ ๋Ÿฌ๋‹][์‹œ๊ณ„์—ด] AR, MA, ARMA, ARIMA์˜ ๋ชจ๋“  ๊ฒƒ - ๊ฐœ๋…ํŽธ


[๋จธ์‹ ๋Ÿฌ๋‹][์‹œ๊ณ„์—ด] AR, MA, ARMA, ARIMA์˜ ๋ชจ๋“  ๊ฒƒ - ์‹ค์ŠตํŽธ


[๋จธ์‹ ๋Ÿฌ๋‹][์ฐจ์›์ถ•์†Œ] ๋ณ€์ˆ˜ ์ถ”์ถœ๋ฒ• - Principal Component Analysis (PCA)


[๋จธ์‹ ๋Ÿฌ๋‹][์ฐจ์›์ถ•์†Œ] ๋ณ€์ˆ˜ ์„ ํƒ๋ฒ•


[ํŒŒ์ดํ† ์น˜] ํŒŒ์ดํ† ์น˜ ๊ธฐ์ดˆ ์š”์†Œ (Autograd๋ž€)


[๊ฟ€ํŒ] Velog ๊ธ€์”จ๋ฅผ ๋‚ด ๋งˆ์Œ๋Œ€๋กœ ๋ฐ”๊ฟ”๋ณด์ž! (์ƒ‰์ƒ, ํ˜•๊ด‘ํŽœ) ๐Ÿ”†


[ํŒŒ์ดํ† ์น˜] ํŒŒ์ดํ† ์น˜ ๊ธฐ์ดˆ ์š”์†Œ (ํ…์„œํŽธ)


[Paper Review] Transferring Inductive Bias Through Knowledge Distillation - (3/3)


[๊ฐœ๋…] CPU, GPU, ๊ทธ๋ฆฌ๊ณ  GPU ์›๋ฆฌ


[Paper Review] Visualizing Data using t-SNE


[Paper Review] Transferring Inductive Bias Through Knowledge Distillation - (2/3)


[Paper Review] Transferring Inductive Bias Through Knowledge Distillation - (1/3)


[๊ธ€๋˜ 6๊ธฐ] ๊ธ€์“ฐ๋Š” ๊ฐœ๋ฐœ์ž ๋ชจ์ž„์— ์ฐธ๊ฐ€ํ•˜๊ฒŒ ๋˜์—ˆ์Šต๋‹ˆ๋‹ค!


[๋”ฅ๋Ÿฌ๋‹][๊ฐœ๋…์ •๋ฆฌ] Inductive Bias๋ž€?


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