What is Word Embedding | Generative AI Tutorial for Beginner|ChatGPT Tutorial [Updated 2024]-igmGuru
Channel: igmGuru
Date: 27/03/2024

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Word embedding in generative AI refers to the process of representing words as dense vectors in a continuous vector space. These vectors capture semantic relationships between words, allowing algorithms to understand the meaning and context of words within a corpus of text. 

Generative AI models, such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), or transformers, often employ word embeddings as part of their architecture. These embeddings serve as the input representation for the model, allowing it to process and generate text data more effectively.

Popular word embedding techniques include Word2Vec, GloVe (Global Vectors for Word Representation), and FastText. These methods learn embeddings by considering the co-occurrence statistics of words in a large corpus of text data. The resulting embeddings encode semantic similarities between words, enabling generative AI models to produce more coherent and contextually relevant outputs.