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How do you make yourself stand out?

What is one of the projects you are super proud of being a part of? Why?

Are there any specific algorithms or software that you would recommend for detecting anomalies effectively?

Tell me about a community you are passionate about. If you had a chance to contribute, how would you?

What practices are recommended for overcoming covariate imbalance in machine learning, and how do they aid in enhancing model robustness?

Briefly describe your background and what is your most relevant experience within Dell

What are some of the scenarios in which overfitting and underfitting are prevalent in real-world modelling? What are the implications of these tendencies for the accuracy and performance of the models?

Would you elaborate on the relevance of training and loss graphs in the sphere of neural networks? How do various loss functions shape the loss graph, and what are some prevalent ones? How do the training and loss graphs contrast with one another, and what is their role in optimization and enhancement of model performance?

What's the usual workflow for a Data Science project, from its kickoff to its wrap-up?

In neural networks, are the initial layers or the deeper layers more often affected by the problem of vanishing gradients?