Loading repository data…
sunilcyriac / repository
This repository contains the python coding files in jupyter notebook to analyse the data for Australian states variables like population, unemployment rate house index and per capita income.
A transparent discovery signal based on current public GitHub metadata.
This score does not audit code, security, maintainers, documentation quality, or suitability. Verify the repository and its current documentation before adoption.
README preview is not available for this repository.
Selected from shared topics, language and repository description—not editorial ratings.
milaan9 /
This repository contains Python games that I've worked on. You'll learn how to create python games with AI. I try to focus on creating board games without GUI in Jupyter-notebook.
81/100 healthjanblechschmidt /
This repository contains a number of Jupyter Notebooks illustrating different approaches to solve partial differential equations by means of neural networks using TensorFlow.
80/100 healthintegrativebioinformatics /
This repository contains scNotebooks, a collection of interactive Jupyter and Google Colab notebooks designed to teach and practice single‑cell and spatial transcriptomics. The notebooks guide learners through the complete workflow from introductory steps and single‑cell pipelines to diverse analytical approaches, and FAIR and sharing data
dipanjanS /
This repository will contain the presentation and python jupyter notebooks for the DataHack Summit 2024 conference talk, Improving Real-world Retrieval Augmented Generation Systems, focusing on the key challenges and practical solutions of how to solve them
69/100 healthlaxmimerit /
This repository contains implementations of Retrieval-Augmented Generation (RAG) in Jupyter notebooks. It includes examples of building chatbots with and without history, processing PDFs with RAG, and using DeepSeek models for local RAG and financial document analysis.
62/100 healthStephanRhode /
This repository contains jupyter notebooks and python code for KIT course: Python Algorithms for Automotive Engineering
58/100 health