Notebook images for ODH

NVIDIA Riva runnable tutorials

The repository includes Notebook files and documents of the course I completed in NVIDIA Deep Learning Institute. Feel free to acess and work with the Notebooks and other files.

Setting up a Jupyterhub Dockercontainer to spawn Jupyter Notebooks with GPU support (containing Tensorflow, Pytorch and Keras)

Jupyter Notebook with Tensorflow, Pytorch and Keras with GPU support

🚜 🚙Programming with Cuda on IPython Notebook

📁 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 🚀

Nvidia DLI Getting Started with AI on Jetson Nano course notebooks

This repository contains notebooks showing how to perform mixed precision training in tf.keras 2.0

Jupyter Notebook Container with CUDA Processing Enabled

nvidia xorg for dell g3 15 (double screen intel+nvidia)

Jupyter notebooks from the "Building Real-Time Video AI Applications" course by NVIDIA DLI.

H.E.I.M.D.A.L.L looks at fleet telemetry and gives you natural-language insights. GPU data loading (cuDF), local LLM inference (Gemma 2), and production NIM on GKE. Open the notebooks, run cells, get answers! Quick start should not take longer than 10 minutes and the T4 path is completely free!

Launch softwares and games with Nvidea in the Slackware in Notebooks with Nvidia + Intel (hybrid video card).

Final assignment from NVIDIA’s Introduction to Deep Learning course. Hands-on Jupyter Notebook showcasing neural network training, evaluation, and optimization.

A Python-based analysis of NVIDIA's stock performance, focusing on price trends, volatility metrics (ATR, Bollinger Bands), and trading volume correlations.

Practical guide to clustering NVIDIA DGX Spark nodes for multi-node vLLM inference (NCCL, RoCE, Ray), with troubleshooting playbooks and step-by-step notebooks.

This project involves analyzing the relationship between Nvidia and AI technologies. The notebook covers data pre-processing steps, including importing necessary libraries and loading data. Further analysis and insights into Nvidia's impact on AI and related trends are provided.

This Jupyter Notebook analyzes NVIDIA (NVDA) stock data to compute risk metrics like Value at Risk (VaR), Expected Shortfall (ES), Sharpe Ratio, Sortino Ratio, Maximum Drawdown, and Beta. It includes visualizations of daily returns and metrics to assess the stock’s risk and performance.