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How do you approach gradient issues in deep learning, particularly the vanishing and exploding problems?

How do neural networks utilize attention mechanisms to enhance their performance?

Find the maximum sum path that goes through the root of a tree and return its sum.

Let's talk about your predictive modeling approach when it comes to determining whether future customers will buy a particular software. What variables and insights would make up your dataset?

There are several ways to optimize hyperparameters in a machine learning model. How would you compare and contrast grid search CV and random search CV for this task?

If given a binary and continuous variable, what's your method to find the correlation between the two?

Would you elucidate the meaning of training and loss graphs pertaining to neural networks? How does the choice of loss function impact the shape of the loss graph, and what are some extensively used ones? How do the training and loss graphs differ, and what role do they play in optimizing and maximizing model performance?

How do information theory principles aid in evaluating different language models? Could you give a practical example of such an evaluation?

Could you share examples of any obstacles you've faced while implementing supervised machine learning and how you've been able to solve them?