Impact Analytics Deep Learning Engineer Interview Questions, Process, and Tips

Different types of regression analysis include linear regression, logistic regression, polynomial regression, ridge regression, and lasso regression.

• Linear regression: Predicts a continuous outcome based on one or more input features.

• Logistic regression: Predicts the probability of a binary outcome.

• Polynomial regression: Fits a curve to the data by including polynomial terms.

• Ridge regression: Adds a penalty term to the...read more

Confusion matrix is a table used to evaluate the performance of a classification model.

• It is a matrix with rows representing the actual class and columns representing the predicted class.

• It helps in visualizing the performance of a classification model by showing the counts of true positive, true negative, false positive, and false negative predictions.

• It is commonly used in machine learning to assess the quality of th...read more

Transformer based models use self-attention mechanism to capture long-range dependencies in data.

• Transformer models consist of encoder and decoder layers.

• Self-attention mechanism allows each word to attend to all other words in the input sequence.

• Positional encoding is added to input embeddings to provide information about the position of words.

• Transformer models have achieved state-of-the-art results in various NLP ta...read more

Various NLP techniques for text extraction include Named Entity Recognition, Part-of-Speech tagging, and Dependency Parsing.

• Named Entity Recognition (NER) identifies entities such as names, dates, and locations in text.

• Part-of-Speech tagging assigns grammatical categories to words in a sentence.

• Dependency Parsing analyzes the grammatical structure of a sentence to identify relationships between words.