Recommender systems predict user preferences for products based on historical behavior and are widespread in online retail, social media, streaming video and music platforms, and more.

However, building, training, and optimizing a recommender system model can be quite time consuming for novices and experts alike. To achieve a state-of-the-art model, you need to set up the right environment, train with the correct hyperparameters, and optimize it to achieve the desired accuracy. In this webinar, you’ll explore the NVIDIA® NGC™ catalog—which has a whole host of GPU-optimized AI software such as containers, pre-trained models, and use case-based Jupyter notebooks—and how to use these resources to kickstart your AI journey.

• About the NGC catalog and how it helps accelerate your AI workflows

• How to leverage a Jupyter notebook containing a pre-trained recommender system model that can be used to recommend a movie based on a user’s viewing history

• How to refine the model by retraining it using your own hyperparameters and test it using your own checkpoints

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