Learn How to Build Intelligent Recommender Systems

Deep learning-based recommender systems are the secret ingredient behind personalized online experiences and powerful decision support tools in retail, entertainment, healthcare, finance, and other industries. 

Recommender systems work by understanding the preferences, previous decisions, and other characteristics of many people. For example, recommenders can help a streaming media service understand the types of movies an individual enjoys, which movies they’ve actually watched, and the languages they understand. Training a neural network to generalize this mountain of data and quickly provide specific recommendations for similar individuals or situations requires massive amounts of computation, which can be accelerated dramatically by GPUs.

Organizations seeking to provide more delightful user experiences, deeper engagement with their customers, and better informed decisions can realize tremendous value by applying properly designed and trained recommender systems.

The NVIDIA Deep Learning Institute (DLI) is offering instructor-led, hands-on training on the fundamental tools and techniques for building highly effective recommender systems, as well as how to deploy GPU-accelerated solutions for real-time recommendations. 

By participating in this workshop, you’ll learn how to:

  • Build a content-based recommender system using the open-source cuDF library and Apache Arrow
  • Construct a collaborative filtering recommender system using alternating least squares (ALS) and CuPy
  • Design a wide and deep neural network using TensorFlow 2 to create a hybrid recommender system
  • Optimize performance for both training and inference using large, sparse datasets
  • Deploy a recommender model as a high-performance web service

This training will be available to the public at the GPU Technology Conference, October 5-9. Spots are limited. Early Bird Rate ends Sept. 25.

Learn More 


Visit the NVIDIA DLI homepage for a full list of online and instructor-led training.