HUDL Data Analyst Interview Questions and Answers

RNNs process sequential data, while CNNs excel in spatial data analysis, like images.

• RNNs are designed for sequential data, such as time series or text.

• CNNs are optimized for grid-like data, particularly images.

• RNNs use loops to maintain memory of previous inputs, making them suitable for tasks like language modeling.

• CNNs utilize convolutional layers to detect patterns and features in images, useful in image classifica...read more

A neural network is a computational model inspired by the human brain, used for pattern recognition and data analysis.

• Neural networks consist of layers of interconnected nodes (neurons) that process input data.

• They function by adjusting weights through a process called backpropagation to minimize error.

• Common strategies to avoid overfitting include using dropout layers, which randomly deactivate neurons during training...read more

Addressing skewed datasets in classification requires techniques like resampling, using appropriate metrics, and algorithm adjustments.

• Use resampling techniques: Oversample the minority class (e.g., SMOTE) or undersample the majority class.

• Implement cost-sensitive learning: Assign higher misclassification costs to the minority class to guide the model.

• Choose appropriate evaluation metrics: Use F1-score, precision-recal...read more