Amaljith & Associates Interview Questions and Answers

Attention in data science refers to the mechanism that allows models to focus on specific parts of the input data.

• Attention mechanisms help models to weigh the importance of different input features.

• They are commonly used in natural language processing tasks such as machine translation and text summarization.

• Attention can improve the performance of models by allowing them to selectively focus on relevant information.

• Examples of attention mechanisms include self-attention in t...read more

Efficiency of LLMs can be judged based on various factors such as accuracy, speed, resource consumption, and interpretability.

• Evaluate accuracy by comparing LLM predictions with ground truth labels

• Assess speed by measuring the time taken for LLM to process data

• Analyze resource consumption in terms of memory and computational power usage

• Consider interpretability by examining how easily LLM decisions can be understood

• Use metrics like precision, recall, F1 score, and computation...read more

Handling class imbalance involves techniques like resampling, using different algorithms, and adjusting class weights.

• Use resampling techniques like oversampling the minority class or undersampling the majority class.

• Try using different algorithms that are less sensitive to class imbalance, such as Random Forest or XGBoost.

• Adjust class weights in the model to give more importance to the minority class.

KNN is a supervised learning algorithm used for classification and regression, while Kmeans is an unsupervised clustering algorithm.

• KNN is a supervised learning algorithm that classifies a new data point based on the majority class of its k-nearest neighbors.

• Kmeans is an unsupervised clustering algorithm that partitions data into k clusters based on similarity.

• KNN requires labeled training data, while Kmeans does not require labeled data.

• KNN is a lazy learner, meaning it does...read more

A large language model is a type of artificial intelligence model that is capable of understanding and generating human language at a large scale.

• Large language models use deep learning techniques to process and generate text.

• Examples include GPT-3 (Generative Pre-trained Transformer 3) and BERT (Bidirectional Encoder Representations from Transformers).

Precision is the ratio of correctly predicted positive observations to the total predicted positive observations.

• Precision is calculated as TP / (TP + FP), where TP is true positives and FP is false positives.

• It measures the accuracy of positive predictions made by the model.

• A high precision indicates that the model is good at predicting positive cases without many false positives.

• For example, in a binary classification problem, if the model predicts 100 positive cases and 90...read more