Loading repository data…
yandisa21 / repository
This Jupyter Notebook provides a comprehensive analysis of student performance based on various socio-economic and demographic factors. The dataset includes information such as gender, parental education level, lunch type, test preparation course completion, and scores in math, reading, and writing.
This project explores and analyzes student performance data using Python and data visualization tools. The goal is to uncover insights into how various factors—like parental education level, lunch type, and test preparation—affect student scores in math, reading, and writing.
The dataset includes the following features:
git clone https://github.com/yourusername/student-performance-analysis.git
cd student-performance-analysis
Selected from shared topics, language and repository description—not editorial ratings.
gordicaleksa /
The purpose of this repo is to make it easy to get started with JAX, Flax, and Haiku. It contains my "Machine Learning with JAX" series of tutorials (YouTube videos and Jupyter Notebooks) as well as the content I found useful while learning about the JAX ecosystem.
This jupyter notebook is used to demonstrate our recent work, "DeepLOB: Deep Convolutional Neural Networks for Limit Order Books", published in IEEE Transactions on Singal Processing. We use FI-2010 dataset and present how model architecture is constructed here. The FI-2010 is publicly avilable and interested readers can check out their paper.
NVIDIA /
This repo contains CUDA-Q Academic materials, including self-paced Jupyter notebook modules for building and optimizing hybrid quantum-classical algorithms using CUDA-Q.
weisscharlesj /
Scientific Computing for Chemists with Python is a free book for teaching basic coding skills to chemists using Python, Jupyter notebooks, and the other Python software. This textbook teaches a variety of Python packages including NumPy, SciPy, matplotlib, pandas, seaborn, nmrglue, SymPy, scikit-image, scikit-learn, and others.
vbartle /
This is a companion to the ‘Mathematical Foundations’ section of the book, Mathematics for Machine Learning by Marc Deisenroth, Aldo Faisal and Cheng Ong, written in python for Jupyter Notebook.
HPInc /
📁 This repository hosts a growing collection of AI blueprint projects that run end-to-end using Jupyter notebooks, MLflow deployments, and Streamlit web apps.🛠️ All projects are built using HP AI Studio with ❤️ If you find this useful, please don’t forget to star the repository ⭐ and support our work 🚀