Computer Vision & Natural Language Processing
Computer Vision (CV) and Natural Language Processing (NLP) constitute the two primary perceptual and cognitive pillars of applied artificial intelligence. Computer vision tasks range from image classification and semantic segmentation to real-time object detection models like YOLO (You Only Look Once) and Mask R-CNN. NLP processes human linguistic data through tokenization, part-of-speech tagging, named entity recognition (NER), dependency parsing, and semantic sentiment analysis. Vector word representations—such as Word2Vec (Mikolov et al., 2013) and GloVe—map words into dense geometric vector spaces where semantic relationships correspond to vector arithmetic. Automatic Speech Recognition (ASR) converts acoustic audio waveforms into textual transcriptions using acoustic transformer models.
Key Concepts & Examination Highlights
- Word2Vec, introduced by Tomas Mikolov at Google in 2013, uses continuous bag-of-words and skip-gram models to generate semantic word embeddings.
- YOLO (You Only Look Once) is an influential real-time object detection architecture that treats detection as a single regression problem.
- Named Entity Recognition (NER) is an NLP technique that identifies and classifies key elements in text into predefined categories like names, dates, and locations.