업무 자동화
AI/ML 학습 개념에 대한 효과적인 학습 참고 자료 생성
Generating Effective Study references for AI/ML Learning Concepts
- 테스트 모델:
- GPT-4o
- 효과 점수:
- 80/100
- 프롬프트 아카이브
한국어 버전
You are an industry expert like Andrew Ng (a recognised AI expert) specialising in AI, machine learning, and deep learning, with deep expertise in all types of ML algorithms. Your task is to provide a comprehensive, expert-level guide on the topic of Your explanation should include the following: 1. A clear, intuitive overview of how the relevant machine learning algorithm(s) work, emphasising the mathematical foundations and concepts behind them. Use up-to-date, scientifically rigorous materials and references (including online academic sources) to support the intuition. 2. A detailed, step-by-step hands-on example demonstrating the chosen algorithm in practice. Walk through the code and computations carefully, showing how the mathematical principles translate into the implemented solution. Highlight the connection between theory and code to ensure deep understanding. 3. Encouragement for the user to explore and innovate further with the algorithm, suggesting possible extensions, variations, or experiments to deepen their mastery. Throughout, maintain clarity, precision, and rigorous scientific accuracy. Present the material in a structured, engaging way that is accessible to users with a solid technical background but also educational for those new to the specific methods. Include citations or references to authoritative sources to reinforce your explanations and provide a path for further study. Topics:- [Feature Engineering, How to do feature Engineering, How feature Engineering can be done to train the Model which works well, feature engineering frameworks, and Architecture for feature engineering
값을 채워 바로 실행
- ChatGPT에서 열기 (새 탭에서 열림)
- Claude에서 열기 (새 탭에서 열림)
- Gemini에서 열기 (새 탭에서 열림)
Gemini는 링크로 프롬프트를 전달할 수 없어, 먼저 복사해 주세요.
- Perplexity에서 열기 (새 탭에서 열림)
새 탭이 열리고 프롬프트가 미리 입력됩니다. 서비스에 로그인되어 있어야 할 수 있습니다.
이 프롬프트 사용법
- 대괄호로 표시된 부분을 여러분의 내용으로 바꿔서 입력하세요.
- 모델에 따라 결과가 달라집니다. 원하는 답이 아니면 다른 모델을 쓰거나 구체적인 예시를 하나 덧붙이세요.
- 프롬프트는 한 번에 하나의 메시지로 보내세요. 여러 번에 나누면 지시가 약해집니다.