Word Embedding
Word embedding is a technique in natural language processing (NLP) that represents words as dense vectors of real numbers in a continuous vector space, capturing semantic and syntactic relationships between words. It transforms textual data into numerical form that machine learning models can process, enabling algorithms to understand word meanings based on context and usage patterns. Common methods include Word2Vec, GloVe, and FastText, which learn embeddings from large text corpora.
Developers should learn word embedding when working on NLP tasks such as text classification, sentiment analysis, machine translation, or recommendation systems, as it provides a foundational representation for words that improves model performance. It is essential for building models that require understanding of language semantics, like chatbots or search engines, and is widely used in deep learning frameworks like TensorFlow and PyTorch for preprocessing text data.