From the course: Building Recommender Systems with Machine Learning and AI
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Get GRU4Rec working on your desktop - Python Tutorial
From the course: Building Recommender Systems with Machine Learning and AI
Get GRU4Rec working on your desktop
- GRU RNNs are a complicated piece. And building a GRU based recurrent neural network is actually a very complicated task as well. It would be crazy to try and write it from scratch if you don't have to. As with most scientific progress, you want to build upon the work of others whenever you can. Just don't cross the line of stealing someone else's work and calling it your own. So your challenge is to do exactly that. Build upon the work of others, and get a GRU RNN running on our MovieLens data. The authors of the paper published code along with it, but it was written in Theano, which is kind of like Tensorflow, but Tensorflow is what ultimately took off, not Theano. Even though the paper is only a couple of years old, a lot has changed in that time. So if you were to adapt the ideas on this paper to Tensorflow, you might have to learn Theano first and port everything over to Tensorflow. But it turns out, someone else has already done that too, and that's what this Bitly link will…
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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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