NoBroker Senior Data Scientist Interview Questions, Process, and Tips

Encoders and decoders are used to convert data from one format to another, such as encoding text into binary or decoding encrypted messages.

• Encoders convert data from one format to another, such as text to binary.

• Decoders reverse the process, converting encoded data back to its original format.

• Examples include Base64 encoding for email attachments and encryption algorithms like AES for secure communication.

1D CNNs are used in signal processing, time series analysis, speech recognition, and natural language processing.

• Signal processing: analyzing signals such as audio, EEG, ECG

• Time series analysis: forecasting stock prices, weather patterns

• Speech recognition: converting spoken language to text

• Natural language processing: sentiment analysis, text classification

Boosting algorithms work by combining multiple weak learners to create a strong learner.

• Boosting algorithms train multiple weak learners sequentially, with each subsequent learner focusing on the mistakes made by the previous ones.

• The final prediction is made by combining the predictions of all the weak learners, usually weighted based on their individual performance.

• Examples of boosting algorithms include AdaBoost, Gr

1x1 kernels in CNN help in reducing the number of parameters and computational cost while increasing the non-linearity of the network.

• 1x1 kernels are used to perform dimensionality reduction by combining features from different channels.

• They help in increasing the non-linearity of the network by introducing additional non-linearities through activation functions.

• 1x1 convolutions are computationally efficient compared t...read more

AUC stands for Area Under the Curve and indicates the performance of a classification model.

• AUC is a metric used to evaluate the performance of a classification model.

• It measures the ability of the model to distinguish between positive and negative classes.

• AUC ranges from 0 to 1, where a higher value indicates better performance.

• An AUC of 0.5 suggests the model is no better than random guessing, while an AUC of 1 indic