인공지능을-배우려고-시간을-낭비하지-마세요-제가-이미-다-해-드렸어요-단-하나의-목로

펭귄 (@babybluecream)

2025-12-27 | ❤️ 3710 | 🔁 1686

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인공지능을 배우려고 시간을 낭비하지 마세요. 📘📚

제가 이미 다 해 드렸어요.

단 하나의 목록으로. 혼란은 전혀 없고. 군더더기도 없다.

📹 동영상:

1. LLM 소개: https://lnkd.in/dMqbaZdK

2. LLM 학위 취득 과정 (초기 단계): https://lnkd.in/dYYwEhYy

3. 에이전트형 AI 개요(스탠포드): https://lnkd.in/dArmMt2i

4. 에이전트 구축 및 평가: https://lnkd.in/dBWd2W8u

5. 효과적인 에이전트 구축: https://lnkd.in/dHfdebqw

6. MCP를 사용하는 건축 에이전트: https://lnkd.in/dXuNHrRJ

7. 에이전트를 처음부터 구축하기: https://lnkd.in/da3ANw3w

8. Philo 에이전트: https://lnkd.in/dq-BfZE5

🗂️ 저장소

1. GenAI 에이전트: https://lnkd.in/d3UDtwwv

2. 마이크로소프트의 초보자를 위한 AI 에이전트: https://lnkd.in/dHvTmJnv

3. 신속 엔지니어링 가이드: https://lnkd.in/gJjGbxQr

4. 대규모 언어 모델 실습: https://lnkd.in/dxaVF86w

5. 초보자를 위한 AI 에이전트: https://lnkd.in/dHvTmJnv

6. GenAI 에이전트 https://lnkd.in/dEt72MEy

7. ML로 제작됨: https://lnkd.in/d2dMACMj

8. 실습 중심의 AI 엔지니어링: https://lnkd.in/dgQtRyk7

9. 멋진 생성형 AI 가이드: https://lnkd.in/dJ8gxp3a

10. 머신러닝 시스템 설계: https://lnkd.in/dEx8sQJK

11. 마이크로소프트의 초보자를 위한 머신 러닝: https://lnkd.in/dBj3BAEY

12. LLM 과정: https://lnkd.in/diZgGACG

🗺️ 가이드

1. Google 에이전트 백서: https://lnkd.in/gFvCfbSN

2. Google 에이전트 컴패니언: https://lnkd.in/gfmCrgAH

3. 인류학에 의한 효과적인 에이전트 구축: https://lnkd.in/gRWKANS4 .

4. 클로드 코드의 에이전트 코딩 모범 사례: https://lnkd.in/gs99zyCf

5. OpenAI의 에이전트 구축을 위한 실용 가이드: https://lnkd.in/guRfXsFK

📚 도서:

1. 딥러닝 이해하기: https://lnkd.in/dgcB68Qt

2. LLM을 처음부터 구축하기: https://lnkd.in/g2YGbnWS

3. LLM 엔지니어링 핸드북: https://lnkd.in/gWUT2EXe

4. AI 에이전트: 완벽 가이드 - 니콜 코닉스타인: https://lnkd.in/dJ9wFNMD

5. AI 에이전트를 활용한 애플리케이션 구축 - 마이클 알바다: https://lnkd.in/dSs8srk5

6. MCP를 사용하는 AI 에이전트 - 카일 스트라티스: https://lnkd.in/dR22bEiZ

7. AI 공학: https://lnkd.in/gi-mQcXa

📜 논문

1. 반응: https://lnkd.in/gRBH3ZRq

2. 생성 에이전트: https://lnkd.in/gsDCUsWm .

3. 툴포머: https://lnkd.in/gyzrege6

4. 사고의 연쇄 촉진: https://lnkd.in/gaK5CXzD .

🧑🏫 강좌:

1. HuggingFace의 에이전트 과정: https://lnkd.in/gmTftTXV

2. 인류를 포함한 MCP: https://lnkd.in/geffcwdq

3. Pinecone을 이용한 벡터 데이터베이스 구축: https://lnkd.in/gCS4sd7Y

4. 임베딩에서 앱까지 벡터 데이터베이스: https://lnkd.in/gm9HR6_2

5. 에이전트 메모리: https://lnkd.in/gNFpC542

재공유 ㄱㄱ

🔗 원본 링크

• https://lnkd.in/dMqbaZdK
• https://lnkd.in/dYYwEhYy
• https://lnkd.in/dArmMt2i
• https://lnkd.in/dBWd2W8u
• https://lnkd.in/dHfdebqw
• https://lnkd.in/dXuNHrRJ
• https://lnkd.in/da3ANw3w
• https://lnkd.in/dq-BfZE5
• https://lnkd.in/d3UDtwwv
• https://lnkd.in/dHvTmJnv
• https://lnkd.in/gJjGbxQr
• https://lnkd.in/dxaVF86w
• https://lnkd.in/dHvTmJnv
• https://lnkd.in/dEt72MEy
• https://lnkd.in/d2dMACMj
• https://lnkd.in/dgQtRyk7
• https://lnkd.in/dJ8gxp3a
• https://lnkd.in/dEx8sQJK
• https://lnkd.in/dBj3BAEY
• https://lnkd.in/diZgGACG
• https://lnkd.in/gFvCfbSN
• https://lnkd.in/gfmCrgAH
• https://lnkd.in/gRWKANS4
• https://lnkd.in/gs99zyCf
• https://lnkd.in/guRfXsFK
• https://lnkd.in/dgcB68Qt
• https://lnkd.in/g2YGbnWS
• https://lnkd.in/gWUT2EXe
• https://lnkd.in/dJ9wFNMD
• https://lnkd.in/dSs8srk5
• https://lnkd.in/dR22bEiZ
• https://lnkd.in/gi-mQcXa
• https://lnkd.in/gRBH3ZRq
• https://lnkd.in/gsDCUsWm
• https://lnkd.in/gyzrege6
• https://lnkd.in/gaK5CXzD
• https://lnkd.in/gmTftTXV
• https://lnkd.in/geffcwdq
• https://lnkd.in/gCS4sd7Y
• https://lnkd.in/gm9HR6_2
• https://lnkd.in/gNFpC542

미디어

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🔗 Related

• chain-of-view-makes-vision-language-models-move-through-a
• vlas-nowadays-enable-robotic-manipulation-to-perform
• mvinverse-feed-forward-multi-view-inverse-rendering-in
• uc-berkeley-offers-two-free-courses-on-llm-agents-one-at
• claude-cowork-활용-방식이야-개인마다-입맛에-맞게-anthropic에서-제안한-usecases

인용 트윗

Krishna Agrawal (@Krishnasagrawal)

Stop wasting hours trying to learn AI. 📘📚

I have already done it for you.

With one list. Zero confusion. And no fluff

📹 Videos:

1. LLM Introduction: https://t.co/OLkDaY7H7g

2. LLMs from Scratch: https://t.co/2N91lxtSh1

3. Agentic AI Overview (Stanford): https://t.co/4xX3JiNUlm

4. Building and Evaluating Agents: https://t.co/paoZnUdySm

5. Building Effective Agents: https://t.co/TICgQVUFCk

6. Building Agents with MCP: https://t.co/EPKu3G7Fms

7. Building an Agent from Scratch: https://t.co/U3XSK0Qx85

8. Philo Agents: https://t.co/wGNERv8TpH

🗂️ Repos

1. GenAI Agents: https://t.co/mmvbJpOSlG

2. Microsoft’s AI Agents for Beginners: https://t.co/zQLhaT3Wpl

3. Prompt Engineering Guide: https://t.co/wYFPPKX4H1

4. Hands-On Large Language Models: https://t.co/92ZZJh6MXG

5. AI Agents for Beginners: https://t.co/zQLhaT3Wpl

6. GenAI Agentshttps://lnkd.in/dEt72MEy

7. Made with ML: https://t.co/gvzfW6aKnf

8. Hands-On AI Engineering:https://t.co/DHTvNbpMVk

9. Awesome Generative AI Guide: https://t.co/jfjbl5CQvA

10. Designing Machine Learning Systems: https://t.co/KIV2X9uKQs

11. Machine Learning for Beginners from Microsoft: https://t.co/Gdl4oZxQEY

12. LLM Course: https://t.co/59QVZqGED0

🗺️ Guides

1. Google’s Agent Whitepaper: https://t.co/Psy7vqkVR2

2. Google’s Agent Companion: https://t.co/sdw5mZhsOM

3. Building Effective Agents by Anthropic: https://t.co/euyR1t8Dg0.

4. Claude Code Best Agentic Coding practices: https://t.co/psPsyWWMwH

5. OpenAI’s Practical Guide to Building Agents: https://t.co/67B9XaLolZ

📚Books:

1. Understanding Deep Learning: https://t.co/j5xz5XfkFL

2. Building an LLM from Scratch: https://t.co/TX8BJPpxX0

3. The LLM Engineering Handbook: https://t.co/rSWCiuvZYW

4. AI Agents: The Definitive Guide - Nicole Koenigstein: https://t.co/m3Tq3vE6Go

5. Building Applications with AI Agents - Michael Albada: https://t.co/QM4mYlYdWh

6. AI Agents with MCP - Kyle Stratis: https://t.co/sIqEe430D6

7. AI Engineering: https://t.co/hs4bN4xFAV

📜 Papers

1. ReAct: https://t.co/k3tFy7pbvR

2. Generative Agents: https://t.co/yQp1Tjl94i.

3. Toolformer: https://t.co/RKBauj9GVX

4. Chain-of-Thought Prompting: https://t.co/fVqbr68wvt.

🧑🏫 Courses:

1. HuggingFace’s Agent Course: https://t.co/OYgR6C8HNd

2. MCP with Anthropic: https://t.co/n5nnFc9DUC

3. Building Vector Databases with Pinecone: https://t.co/MLhJnfbdGG

4. Vector Databases from Embeddings to Apps: https://t.co/31zEvnwtfZ

5. Agent Memory: https://t.co/ldYc6RVmMj

Repost for your network ♻️

원본 트윗

Tags

AI-ML LLM