From the course: Building Recommender Systems with Machine Learning and AI
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Evaluating the RBM recommender - Python Tutorial
From the course: Building Recommender Systems with Machine Learning and AI
Evaluating the RBM recommender
- [Instructor] Alight, we've been talking about RBMs for long enough. Let's actually run the thing and see how it does. Select the RBMBakeOff.py file, and take a quick look at it. There's not a lot to talk about. What we're doing is pitting our RBM algorithm with 20 epochs and default hyper parameters against random recommendations. Hit the play button, and go get a cup of coffee, catch up on your messages, whatever you need to do. We did pass true to the evaluate function, so we're going to run all of the top end metrics on everything, which can take quite a bit of time. So pause this video, and resume when you're ready to view the results with me. Okay, there's a lot to digest here. Let's start by looking at the table of all the metrics. The accuracy metrics RMSE and MAE are better than random, but they're not great. We mentioned before that the way we're computing predicted rating values, tend to artificially lower them. The expectation values we end up with don't get any higher…
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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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