Data Science Lead Interview Questions and Answers

Transformers in machine learning are a type of deep learning model that uses self-attention mechanisms to process sequential data.

• Transformers consist of an encoder and a decoder, each composed of multiple layers of self-attention and feedforward neural networks.

• Self-attention allows the model to weigh the importance of different input tokens when making predictions.

• Transformers have been widely used in natural language processing tasks, such as language translation (e.g. Goo...read more

The loss function used in XGBoost is typically the gradient boosting algorithm.

• XGBoost uses the gradient boosting algorithm to minimize the loss function

• Common loss functions include regression loss functions like squared error and logistic loss for classification

• Users can also define custom loss functions in XGBoost

The project utilized various AWS services for data storage, processing, and analysis.

• S3 for data storage

• EC2 for computing resources

• Glue for ETL processes

• Sagemaker for machine learning models

• Athena for querying data in S3