Grubbrr Systems International Machine Learning Engineer Interview Questions and Answers

Bias is error due to overly simplistic assumptions, variance is error due to overly complex models.

• Bias is error introduced by approximating a real-world problem, leading to underfitting.

• Variance is error introduced by modeling the noise in the training data, leading to overfitting.

• High bias can cause a model to miss relevant relationships between features and target variable.

• High variance can cause a model to be overl...read more

Learning rate is a hyperparameter that controls how much we are adjusting the weights of our network with respect to the loss gradient.

• Learning rate determines the size of the steps taken during optimization.

• A high learning rate can cause the model to converge too quickly and potentially miss the optimal solution.

• A low learning rate can cause the model to take a long time to converge or get stuck in a local minimum.

• Com...read more

Transformers are models that process sequential data by learning contextual relationships between words.

• Transformers are a type of deep learning model commonly used in natural language processing tasks.

• They are based on the attention mechanism, allowing them to focus on different parts of the input sequence.

• Examples of transformer models include BERT, GPT, and TransformerXL.