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Building a Winning Deep Learning Recommender System - Grandmaster Series Episode 5

Offered By: Nvidia via YouTube

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Recommender Systems Courses Machine Learning Courses Deep Learning Courses Neural Networks Courses Matrix Factorization Courses Ensemble Methods Courses GPU Acceleration Courses Data Augmentation Courses

Course Description

Overview

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.

Syllabus

– Intro to Episode Five
– Booking.com Challenge Overview & Summar
– Matrix Factorization Ensemble Overview
– Model One, MLP with Session-Based Matrix Factorization
– Model Two, GRU with MultiStage Session-based Matrix Factorization
– Model Three, XLNet with Session-based Matrix Factorization
– Combing All Three Models into One Ensemble
– Data Augmentation Approach
– NVIDIA Merlin is an End-2-End Library for GPU-Accelerated Recommender Systems
– Q&A Session


Taught by

NVIDIA Developer

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