Direktori skill

Temukan skill yang dapat digunakan kembali untuk AI agents.

Cari skill GitHub nyata berdasarkan tugas lalu periksa stars, trust, audit, kategori, dan jalur pemasangan sebelum digunakan.

Setiap rekomendasi tetap terhubung dengan repositori, audit, dan jalur pemasangannya.

Hasil pencarian: jupyter-notebooks

Direktori bahasa Inggris

๐Ÿ™ Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.

76K
Stars
86/100
Kepercayaan
Kategori: agent-frameworksAudit

Jupyter Interactive Notebook

13K
Stars
82/100
Kepercayaan
Kategori: data-analysisAudit

A collection of tutorials on state-of-the-art computer vision models and techniques. Explore everything from foundational architectures like ResNet to cutting-edge models like RF-DETR, YOLO11, SAM 3, and Qwen3-VL.

9.5K
Stars
76/100
Kepercayaan
Kategori: ml-automationAudit

Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

80K
Stars
84/100
Kepercayaan
Kategori: ml-automationAudit

Ready-to-run Docker images containing Jupyter applications

8.4K
Stars
80/100
Kepercayaan
Kategori: data-analysisAudit

22 prompt engineering techniques with hands-on Jupyter Notebook tutorials, from fundamental concepts to advanced strategies for leveraging LLMs.

7.6K
Stars
77/100
Kepercayaan
Kategori: ml-automationAudit

Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts

7.2K
Stars
81/100
Kepercayaan
Kategori: document-processingAudit

Automate code & data workflows with interactive Elixir notebooks

5.8K
Stars
81/100
Kepercayaan
Kategori: document-processingAudit

๐ŸŽˆ Simple reactive notebooks for Julia

5.3K
Stars
81/100
Kepercayaan
Kategori: educationAudit

Create delightful software with Jupyter Notebooks

5.3K
Stars
77/100
Kepercayaan
Kategori: coding-agentsAudit

The fastest way to turn a Jupyter notebook into a beautiful, shareable web app โ€” no callbacks, no frontend, no rewrite.

4.3K
Stars
85/100
Kepercayaan
Kategori: data-analysisAudit

Preswald is a WASM packager for Python-based interactive data apps: bundle full complex data workflows, particularly visualizations, into single files, runnable completely in-browser, using Pyodide, DuckDB, Pandas, and Plotly, Matplotlib, etc. Build dashboards, reports, and notebooks that run offline, load fast, and share like a document.

4.3K
Stars
80/100
Kepercayaan
Kategori: data-analysisAudit