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What is the importance of gradient checking?
Explain the concept of Local Interpretable Model-agnostic Explanations (LIME).
What are convolutional networks? Where can we use them?
What do you mean by Overfitting? How to avoid this?
What key terms and concepts should one understand when working with LLMs?
A problem in a search space Is defined by, a) Initial state b) Goal test c) Intermediate states d) All of the above
Describe 'training set' and 'training test'.
What is Dropout and Batch Normalization?
List down various approaches to machine learning?
How do you choose the right features for a model?
What are the challenges of making deep learning models explainable?
In top-down inductive learning methods how many literals are available? What are they?
How regularly must an algorithm be updated?
What are the challenges in applying AI to environmental issues?
What is transfer learning, and when would you use it?
