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Design a messaging system like WhatsApp.

If you were tasked with building a metric to compare two user's Netflix recommended lists of movie/tv show preferences, how would you approach this challenge?

What's your strategy to calculate the correlation between a binary and continuous variable?

How do you ensure that an imbalanced dataset does not skew your model’s performance?

Can you detail your process for discovering shared ancestors in a family tree of a large extended family?

Can you describe the process of developing a podcast search engine that enables listeners to search for specific topics within show transcripts?

Design an e-commerce website like Amazon.

Could you explain the importance of training and loss graphs for neural networks? How do various loss functions impact the shape of the loss graph, and what are some commonly known ones? What is the contrast between the training and loss graphs, and how do they assist in optimizing and maximizing model performance?

When faced with gradient problems, how do you modify your deep learning algorithms to ensure stable and accurate predictions?

What architecture would you propose for a recommendation engine that adapts to a user's musical preferences, and how would you integrate their listening traits?