From the course: Building Recommender Systems with Machine Learning and AI
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Restricted Boltzmann machines (RBMs) - Python Tutorial
From the course: Building Recommender Systems with Machine Learning and AI
Restricted Boltzmann machines (RBMs)
and recommender systems is the Restricted Boltzmann Machine or RBM for short. It's been in use since 2007, long before AI had its big resurgence, but it's still a commonly cited paper and a technique that's still in use today. Going back to the Netflix prize, the main things Netflix learned was as measured by RMSE and their scores were almost identical. Again, this shouldn't surprise us too much, since you know that you can model matrix factorization as a neural network, but they found that by combining matrix factorization with RBM's, the two of them working together provided even better results. They went from an RMSE of 8.9 to 8.8. A few years ago, Netflix confirmed they were still using RBM's as part of their recommender system that's in production. Let's learn how it works. First of all, if you're serious about using RBM's for recommendations, I recommend tracking down this paper so you can study it later once you understand the general concepts. It's from a team from the…
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Contents
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Intro to deep learning for recommenders2m 19s
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Restricted Boltzmann machines (RBMs)8m 2s
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Recommendations with RBMs, part 112m 46s
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Recommendations with RBMs, part 27m 11s
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Evaluating the RBM recommender3m 44s
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Tuning restricted Boltzmann machines1m 43s
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Exercise results: Tuning a RBM recommender1m 15s
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Auto-encoders for recommendations: Deep learning for recs4m 27s
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Recommendations with deep neural networks7m 23s
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Clickstream recommendations with RNNs7m 23s
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Get GRU4Rec working on your desktop2m 42s
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Exercise results: GRU4Rec in action7m 51s
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Bleeding edge alert: Deep factorization machines5m 49s
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More emerging tech to watch5m 14s
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