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How do you handle high-dimensional data sets in machine learning challenges?

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?

How did you come to find out you needed a new job?

In the role of SAP Data Scientist, what would be the biggest challenge you think you would face?

Can you demonstrate how to create a normally distributed set of 1000 data points with particular mean and variance, conduct summary statistics, and construct a density plot for it?

With the limitations of only having a random number generator, what approach would you take to generate i.i.d. draws from distribution X?

How does Rectified Linear Unit compare to other popular activation functions in terms of model performance?

Are decision trees a reliable tool for making predictions, or do they have limitations?

Does using highly correlated predictor variables in logistic regression affect our ability to make sound statistical conclusions? How do we handle that?

What makes you a good fit for SAP?