From the course: Building Recommender Systems with Machine Learning and AI
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More emerging tech to watch - Python Tutorial
From the course: Building Recommender Systems with Machine Learning and AI
More emerging tech to watch
- [Instructor] While we are still operating under our bleeding edge alert, let's briefly cover a few more recent research topics that are worth keeping an eye on. Some people have applied a neural network model called word2vec, to recommender problems. If you have text data, to work with, it can be an interesting tool. The idea is you start with some words, that represent the context, or user history you want to use to inform your recommendations. But to make it easier to understand let's just start with the problem of completing someones sentence. We can encode all of the words in that sentence. Feed it through an embedding layer and a hidden layer where the learning takes place and run a softmax classifier on the output of the hidden layer to find the probabilities of each word that might be associated with the ones you fed in this input. In this example, we feed in signals associated with the words to boldly go where no one has. And a properly trained word2vec network might…
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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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