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NVIDIA announced in their Blog to offer several new online courses, one of them about Building Intelligent Recommender Systems. The courses are taught by James Maki, Senior Deep Learning Data Scientist at NVIDIA, and by Adam Henryk Grzywaczewski, Senior Deep Learning Solution Architect at NVIDIA. The course will teach you to create “different types of recommender systems: content-based, collaborative filtering, hybrid, and more. Learn how to use the open-source cuDF library, Apache Arrow, alternating least squares, CuPy and TensorFlow 2 to do so.”
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. We’ll cover 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 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, and deploy a recommender model as a high-performance web service. Prerequisites: Intermediate knowledge of and data science experience with Python, including understanding of list comprehension, and familiarity with NumPy and matrix mathematics.