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  • in sql
  • 11 min read

Neural Networks for Recommendation Systems

In this notebook we will look at how to use a neural network approach to making recommendations

  • The user/item pairings are the main source of data used to create recommendations
  • Scalar product of both the user_id and item_id embeddings will be our relevancy scores
  • User film interactions will be positive feedback & negative samples which will be created randomly are our negative samples
  • The dataset is split into two, train will be used to train a model on historical user data, test will be used to provide user recommendations
  • What we will be telling the model is to learn and differentiate between the films they actually watched apart from those they haven’t (ideally)
  • We have already looked at DSSM in a previous notebook , well be simplifying things a little here, not including user and item features and will keep things more simple.
  • in sql
  • 14 min read

SQL Analytics Problem

An interview question related to SQL knowledge from a financial bank which I thought was interesting so decided to share

  • The first part consists of standard SQL knowledge questions
  • The second part consists of a problem in which we will need to create some code for monitoring the number of hours an employee has worked, which we will be doing with python and posgres