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Arthur DANJOU
05963bb605
- 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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2.9 KiB
slug, title, description, publishedAt, readingTime, favorite, tags
| slug | title | description | publishedAt | readingTime | favorite | tags | |||
|---|---|---|---|---|---|---|---|---|---|
| studies | 🎓 Studies Projects | A curated collection of mathematics and data science projects developed during my academic journey. | 2023/09/01 | 1 | true |
|
Studies Projects is a curated collection of academic projects completed throughout my mathematics studies. The repository showcases work in both Python and R, focusing on mathematical modeling, data analysis, and numerical methods.
The projects are organized into two main sections:
- L3 – Third year of the Bachelor's degree in Mathematics
- M1 – First year of the Master's degree in Mathematics
📁 File Structure
-
L3Analyse MatricielleAnalyse MultidimensionnelleCalculs NumériquesÉquations DifférentiellesMéthodes NumériquesProbabilitésProjet NumériqueStatistiques
-
M1Data AnalysisGeneral Linear ModelsMonte Carlo MethodsNumerical MethodsNumerical OptimizationPortfolio ManagementStatistical Learning
🛠️ Technologies & Tools
- Python: A high-level, interpreted programming language, widely used for data science, machine learning, and scientific computing.
- R: A statistical computing environment, perfect for data analysis and visualization.
- Jupyter: Interactive notebooks combining code, results, and rich text for reproducible research.
- Pandas: A data manipulation library providing data structures and operations for manipulating numerical tables and time series.
- NumPy: Core package for numerical computing with support for large, multi-dimensional arrays and matrices.
- SciPy: A library for advanced scientific computations including optimization, integration, and signal processing.
- Scikit-learn: A robust library offering simple and efficient tools for machine learning and statistical modeling, including classification, regression, and clustering.
- TensorFlow: A comprehensive open-source framework for building and deploying machine learning and deep learning models.
- Matplotlib: A versatile plotting library for creating high-quality static, animated, and interactive visualizations in Python.
- RMarkdown: A dynamic tool for combining code, results, and narrative into high-quality documents and presentations.
- FactoMineR: An R package focused on multivariate exploratory data analysis (e.g., PCA, MCA, CA).
- ggplot2: A grammar-based graphics package for creating complex and elegant visualizations in R.
- and my 🧠.