Description:
Dive into the fifth episode of the Grandmaster Series to learn how Kaggle Grandmasters of NVIDIA (KGMON) constructed a winning Deep Learning Recommender System for the Booking.com Data Challenge. Explore the intricacies of building a real-time recommendation system using anonymized accommodation reservation data. Gain insights into Matrix Factorization Ensemble, three distinct models (MLP, GRU, and XLNet with Session-based Matrix Factorization), and their combination into a powerful ensemble. Discover data augmentation techniques and the capabilities of NVIDIA Merlin for GPU-accelerated recommender systems. Benefit from a Q&A session and access six additional resources to deepen your understanding of advanced recommendation systems and GPU-accelerated data science.
Building a Winning Deep Learning Recommender System - Grandmaster Series Episode 5
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#Computer Science
#Machine Learning
#Recommender Systems
#Deep Learning
#Artificial Intelligence
#Neural Networks
#Matrix Factorization
#Ensemble Methods
#Computer Hardware
#GPU Acceleration
#Data Augmentation