Recommender Systems: An Applied Approach using Deep Learning - Inference after Training

The video tutorial explains the process of training a neural network with data and checking its accuracy. It then moves on to the inference stage, where the model is deployed as a service to predict new interactions. The tutorial further elaborates on how a recommendation system uses neural networks to suggest items based on user interaction likelihood. Finally, it introduces the basic concept and mechanism of developing recommendation systems using deep learning.

What is the purpose of the inference part in a neural network?

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How does the trained neural network make predictions about user interactions?

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What is the role of the selection service in the recommendation system?

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How does the recommendation system adapt to a user's previous consumption patterns?

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What are the key components of a generic recommendation system mechanism?

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