目前AI社区和教程主要以英文为主，很少有一个社区能够系统地讲解：
Most AI communities and tutorials are heavily English-centric, and few provide a structured guide that thoroughly covers:
- 经典的传统算法 → 经典的机器学习算法 → 深度学习基础 → 前沿的AI研究与技术
Classical algorithms → Classical machine learning → Deep learning fundamentals → Cutting-edge AI research and technologies.
我们是两个具有交叉学科背景的AI博士，专注于AI+生物医药领域，深耕研究与实践已有四年，积累了丰富的理论和实际经验。
As two interdisciplinary AI PhDs, we have spent four years deeply engaged in the AI + biopharma domain, gaining substantial practical and theoretical expertise.
此外，我们还有一年在市场上创业的经验，专注于开发和应用大语言模型相关技术。
Additionally, we bring one year of entrepreneurial experience, focusing on the development and application of large language model technologies.
我们希望为广大AI爱好者打造一个全面且专业的学习平台，系统分享从基础知识到前沿研究的理论与实践经验。
We aim to create a comprehensive and professional learning platform for AI enthusiasts, sharing systematic knowledge and practical insights from foundational concepts to cutting-edge research.
在我们的学习与研究旅程中，我们不仅成功研发出新型AI架构，还取得了多项具有实际应用价值的突破性成果，包括：
Throughout our journey, we have developed novel AI architectures and achieved groundbreaking, real-world results, including:
- 世界首个：AI一步设计出AAV核衣壳（一种基因治疗载体）。
World's first: AI-enabled one-step design of AAV capsids (a gene therapy vector). - FeatNN模型助力客户筛选出超过10个先导药物。
FeatNN successfully screened over 10 lead compounds for clients.
我们已整理并发布了一系列关于前沿AI大语言模型的简明教程，内容包括：
We have curated and published concise tutorials on cutting-edge AI and large language models, covering topics such as:
- m1: 从零开始训练GPT2模型（Train GPT-2 from Scratch）
- m2: 使用Lora微调Llama3（适配RTX3060）（Fine-tune Llama3 with LoRA on RTX3060）
- m3: 使用1~4张图片进行DreamBooth微调（Train an image generation model with DreamBooth using just 1-4 images）
- m4: Llama3的长上下文Lora微调（Long-context fine-tuning for Llama3 with LoRA）
- m5: 英文大模型迁移到其他语言（Transfer an English LLM to other languages）
- m6: 利用学生-教师模型蒸馏Llama3（Student-teacher distillation with Llama3）
- m7: 大语言模型的安全与保护策略（LLM Guardrails Strategy）
- m8: 从零训练大规模MoE架构（Train a MoE-based large language model from scratch）
此外，多模态模型、自我博弈（Self-play）、以及更多复现性实验将在后续教程中逐步更新，敬请期待。
Upcoming tutorials will include multimodal models, self-play techniques, and reproducibility experiments. Stay tuned!
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系统化知识库 | A Systematic Knowledge Hub
从经典算法到前沿技术，提供易于理解的理论讲解和实操示例。
Comprehensive coverage from classical algorithms to advanced technologies, featuring intuitive explanations and hands-on examples. -
真实案例分享 | Real-World Case Studies
展示AI技术在不同领域的应用案例，包括：
Showcasing practical applications of AI across various domains, such as:- AI算法解决经典问题（Using AI to address classical challenges）
- AI在工业生产中的应用场景（AI in industrial production scenarios）
- 更多应用场景将在后续添加（More use cases to follow）
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学术与产业结合 | Bridging Academia and Industry
探索从研究到落地的全流程，揭示AI如何驱动产业创新。
Exploring the journey from research to deployment, demonstrating how AI drives industry innovation.
加入我们，共同开启AI学习与探索的新篇章！
Join us and embark on a new chapter of AI learning and discovery!
This refined version improves logical flow, readability, and engagement while maintaining the original intent and tone. Let me know if there are further refinements or additions you'd like!