AML Natural Language Processing & Computer Vision Flashcards
6 cards from real AML practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 6 AML Natural Language Processing & Computer Vision flashcards as text
Which NLP technique converts words into dense vector representations that capture semantic relationships?
Answer: Word2Vec / Word Embeddings
Word embeddings like Word2Vec map words to dense vectors where semantically similar words have high cosine similarity.
What does TF-IDF stand for in text feature extraction?
Answer: Term Frequency–Inverse Document Frequency
TF-IDF weighs each word by how often it appears in a document (TF) penalized by how common it is across all documents (IDF).
In computer vision, what is the role of a convolutional layer in a CNN?
Answer: Apply learnable filters to detect local spatial features
Convolutional layers slide learnable filters across the input image to detect local patterns like edges, textures, and shapes.
Which NLP task involves assigning a label (positive, negative, neutral) to a piece of text based on its emotional tone?
Answer: Sentiment Analysis
Sentiment analysis classifies text according to the opinion or emotion expressed, commonly as positive, negative, or neutral.
What is the purpose of max pooling in a convolutional neural network?
Answer: Reduce spatial dimensions while retaining dominant features
Max pooling downsamples feature maps by taking the maximum value in each pooling window, reducing size while preserving strong activations.
Which transformer-based model introduced bidirectional context for language understanding and set new NLP benchmarks in 2018?
Answer: BERT
BERT (Bidirectional Encoder Representations from Transformers) pre-trains on masked language modeling using full left and right context simultaneously.