Overview
- Build deep learning, accelerated computing, and accelerated data science applications for industries such as autonomous vehicles, healthcare, manufacturing, media and entertainment, robotics, smart cities, and more.
- Gain real-world expertise through content designed in collaboration with industry leaders, such as the Children’s Hospital of Los Angeles, Mayo Clinic, PwC, and Uber.
- Access content anywhere, anytime with a fully configured, GPU-accelerated workstation in the cloud.
- Earn an NVIDIA DLI certificate to demonstrate subject matter competency and support career growth.
- Work with the most widely used, industry-standard software, tools, and frameworks.
Prerequisites
- Experience with Python, ideally including pandas and NumPy.
- To gain experience with pandas, we suggest this pandas course on Kaggle.
- To gain experience with data science using Python, we suggest this machine learning course on Kaggle.
- To get experience accelerating data science workflows, we suggest the Accelerating Data Science Workflows with RAPIDS course with DLI.
Delegates will learn how to
- Implement GPU-accelerated data preparation and feature extraction using cuDF and Apache Arrow data frames
- Apply a broad spectrum of GPU-accelerated machine learning tasks using XGBoost and a variety of cuML algorithms
- Execute GPU-accelerated graph analysis with cuGraph, achieving massive-scale analytics in small amounts of time
- Rapidly achieve massive-scale graph analytics using cuGraph routines
Outline
- Read data directly to single and multiple GPUs with cuDF and Dask cuDF.
- Prepare population, road network, and clinic information for machine learning tasks on the GPU with cuDF.
- Use supervised and unsupervised GPU-accelerated algorithms with cuML.
- Train XGBoost models with Dask on multiple GPUs.
- Create and analyze graph data on the GPU with cuGraph.
- Use RAPIDS to integrate multiple massive datasets and perform real-world analysis.
- Pivot and iterate on your analysis as the simulated epidemic provides new data for each simulated day.
Data, Analytics & AI learning paths
Want to boost your career in data, analytics and AI? Click on the roles below to see QA's learning pathways, specially designed to give you the skills to succeed.
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