效率办公
为人工智能/机器学习学习概念生成有效的学习参考资料
Generating Effective Study references for AI/ML Learning Concepts
- 测试模型:
- GPT-4o
- 有效性评分:
- 80/100
- 提示词归档
中文版本
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
填入内容并运行
- 在 ChatGPT 中打开 (在新标签页中打开)
- 在 Claude 中打开 (在新标签页中打开)
- 在 Gemini 中打开 (在新标签页中打开)
Gemini 无法通过链接接收提示词,请先复制。
- 在 Perplexity 中打开 (在新标签页中打开)
将在新标签页打开并已填入提示词。可能需要先登录该服务。
如何使用此提示词
- 发送前,请将方括号中的内容替换为你自己的信息。
- 不同模型的结果会有差异。若输出不理想,可换一个模型或补充一个具体示例。
- 请将提示词放在一条消息中发送。分多次发送会削弱指令效果。