deerwork-ai
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deer-workflow
An open-source Dynamic Workflow runtime that combines deterministic TypeScript orchestration with replaceable Agent runtimes.
AI 仓库情报摘要
FR-AI / ANALYSIS为什么值得关注
该课程提供了分阶段的学习路径，既包含基础理论，也涵盖前沿主题（如Flash Attention、代理系统、模型评估），并配有清晰的每日指导和复习日。
适合谁使用
- 刚接触AI/ML的自学开发者
- 需要结构化AI学习计划的大学生
- 正在转型AI工程的软件工程师
- 寻找团队技能提升资源的技术负责人
典型使用场景
- 个人100天结构化学习
- 开发团队的内部培训或训练营
- 刷新RAG、推理优化等现代AI知识点
- AI/ML工程岗位面试准备
项目优势
- 涵盖从回归到计算机使用代理的广泛主题
- 每一天都链接到独立、自包含的博客文章
- 包含复习日以巩固学习效果
- 通过#100DaysOfAI公开打卡提高坚持率
使用前须知
- 完全依赖外部博客文章，未提供代码或交互式练习
- 所有内容来自单一来源（Outcome School），可能存在偏见
- 仓库内没有社区讨论或问答机制
README 快速开始
100 Days of AI
Take the 100 Days of AI challenge - learn one AI concept a day, from machine learning basics to AI agents in production, in 100 days.
Prepared and maintained by the Founder of Outcome School: Amit Shekhar
How to take the challenge
The rules are simple:
- Learn one concept every day. Each day maps to one blog - read it, understand it, and take your time with the examples.
- Spend 30 to 60 minutes a day. That is all it takes.
- Write a short note every day in your own words. Teaching yourself on paper is the fastest way to make a concept stick.
- Share your progress publicly on X or LinkedIn with the hashtag #100DaysOfAI. Public commitment keeps you consistent.
- If you miss a day, do not restart. Continue from where you left off. Consistency beats perfection.
- Revision days have no new reading. Use them to revise, rewrite your notes, and let the concepts settle.
Before Day 1
Before starting the challenge, watch this video to get the big picture of AI Engineering - it will help you see where every day of this challenge fits.
Let's get started: AI Engineering Explained: LLM, RAG, MCP, Agent, Fine-Tuning, Quantization
Phase 1: Machine Learning Foundations (Days 1-10)
Everything in AI stands on machine learning. In this phase, we will learn what machine learning is and build the vocabulary we will use for the next 90 days.
| Day | Topic | Resource |
|---|---|---|
| 1 | Machine Learning | What is Machine Learning? |
| 2 | Supervised vs Unsupervised Learning | Supervised vs Unsupervised Learning |
| 3 | Regression | Linear Regression vs Logistic Regression |
| 4 | Feature Engineering | Feature Engineering for Machine Learning |
| 5 | Loss Functions | What Are L1 and L2 Loss Functions? |
| 6 | Regularization | Regularization In Machine Learning |
| 7 | Bias in Neural Networks | [What is Bias In Artificial Neural Network?](https://outcomeschool.com/blog/ |
项目描述
Take the 100 Days of AI challenge - learn one AI concept a day, from machine learning basics to AI agents in production, in 100 days.
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