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Walk me through a Monte Carlo simulation to estimate Pi.

In your experience, have you found Rectified Linear Unit to be a reliable activation function for various deep learning tasks?

What's the usual workflow for an ML project, from its kickoff to its wrap-up?

What are batch normalization and dropout, and why are they important in neural networks?

When it comes to managing web traffic, how do you determine whether to use a load balancer or a reverse proxy?

Can you discuss the relevance of training and loss graphs in the context of neural networks? How do various loss functions alter the shape of the loss graph, and what are some commonly utilized ones? How do the training and loss graphs contrast with each other, and how do they facilitate model optimization and performance?

How do you plan to convert a minesweeper's array of mines into an array reflecting the count of neighboring mines?

Can you discuss your strategies for managing skewed data during model assessment and the metrics that apply?

What is the discrepancy between the overviews of Logistic Regression and SVM?

Can you describe the unique characteristics of CNNs as opposed to RNNs?