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Arthur DANJOU
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- Created a new article on "Understanding AI Agents, LLMs, and RAG" detailing the synergy between AI agents, LLMs, and Retrieval-Augmented Generation. - Added an introductory article on "What is Machine Learning?" covering types, model selection, workflow, and evaluation metrics. chore: setup ESLint and Nuxt configuration - Added ESLint configuration for code quality. - Initialized Nuxt configuration with various modules and settings for the application. chore: initialize package.json and TypeScript configuration - Created package.json for dependency management and scripts. - Added TypeScript configuration for the project. feat: implement API endpoints for activity and stats - Developed API endpoint to fetch user activity from Lanyard. - Created a stats endpoint to retrieve Wakatime coding statistics with caching. feat: add various assets and images - Included multiple images and assets for articles and projects. - Added placeholder files to maintain directory structure. refactor: define types for chat, lanyard, time, and wakatime - Created TypeScript types for chat messages, Lanyard activities, time formatting, and Wakatime statistics.
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slug, title, description, publishedAt, readingTime, tags
| slug | title | description | publishedAt | readingTime | tags | ||
|---|---|---|---|---|---|---|---|
| monte-carlo-project | 💻 Monte Carlo Methods Project | A project to demonstrate the use of Monte Carlo methods in R. | 2024/11/24 | 3 |
|
This is the report for the Monte Carlo Methods Project. The project was done as part of the course Monte Carlo Methods at the Paris-Dauphine University. The goal was to implement different methods and algorithms using Monte Carlo methods in R.
Methods and algorithms implemented:
- Plotting graphs of functions
- Inverse c.d.f. Random Variation simulation
- Accept-Reject Random Variation simulation
- Random Variable simulation with stratification
- Cumulative density function
- Empirical Quantile Function
You can find the code here: Monte Carlo Project Code