Interview Questions

Hari priya K. - "Hey Grandma, you've had a lot of experience with infants, haven't you? When they were babies, you taught them how to chew in their first six months. This initial phase is like giving them data. Once they learned how to chew, they could handle any food you gave them. Next, you refined their learning by teaching them that they should only chew on food. This is like refining the data so they understand what is relevant. Then, a few months later, they started crawling and walking, learning by observ"See full answer

TreeOfWisdom - "Should this question be BST, not just BT? Otherwise it would not be possible to reconstruct the tree solely based on the array regardless of its order"See full answer

Matt M. - "Given the dataset does not contain many labels, it implies we cannot directly use supervised learning. I would ask more about the type of dataset we are given. Is it images, text, etc? This may inform the types of transformations we do the dataset. I can see two approaches to training Given the labels we do have, we can find a method to generate labels for the other unlabeled data. This likely will introduce some error since they may not be true labels, but it at least allows processing the"See full answer

Vishwanath K. - "DevOps Engineer Interview Questions for 3+ yrs experience candidate"See full answer

Shibin P. - "Effective loss functions for computer vision models vary depending on the specific task, some commonly used loss functions for different tasks: Classification Cross-Entropy Loss:Used for multi-class classification tasks. Measures the difference between the predicted probability distribution and the true distribution. Binary Cross-Entropy Loss:Used for binary classification tasks. Evaluates the performance of a model by comparing predicted probabilities to the true binary labe"See full answer