From the course: Building Recommender Systems with Machine Learning and AI
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Intro to deep learning for recommenders - Python Tutorial
From the course: Building Recommender Systems with Machine Learning and AI
Intro to deep learning for recommenders
- So now that you understand the theory behind deep learning and neural networks, let's finally talk about how these techniques can be applied to recommender systems. You can imagine that with all the buzz around artificial intelligence lately, there's a lot of current research on applying neural networks to recommender systems. But is it all hype? Let's get something out of the way right upfront. Just because a new technology is hot, doesn't mean that it's the right solution for every problem. You can train a neural network with user ratings or purchases, and use it to make recommendations, but should you? As we've seen, deep learning can be very good at recognizing patterns in a way similar to how our brain may do it. It's good at things like image recognition and predicting sequences of events. But is making recommendations really a pattern recognition problem? Think back to when we talked about TensorFlow. It doesn't really know much about neural networks, per se, you actually use…
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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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