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History Initial developments. Generative pretraining (GP) was a long-established concept in machine learning applications. It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.
Word2vec is a technique in natural language processing (NLP) for obtaining vector representations of words. These vectors capture information about the meaning of the word based on the surrounding words. The word2vec algorithm estimates these representations by modeling text in a large corpus. Once trained, such a model can detect synonymous ...
Research in machine learning. Headquarters. Toronto, Ontario, Canada. Employees. 714 [1] Website. www .vectorinstitute .ai. The Vector Institute is a private, non-profit artificial intelligence research institute in Toronto focusing primarily on machine learning and deep learning research. As of 2023, it consists of 143 faculty members and ...
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v. t. e. A vector database, vector store or vector search engine is a database that can store vectors (fixed-length lists of numbers) along with other data items. Vector databases typically implement one or more Approximate Nearest Neighbor (ANN) algorithms, [1] [2] so that one can search the database with a query vector to retrieve the closest ...
In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon. [1] Choosing informative, discriminating and independent features is a crucial element of effective algorithms in pattern recognition, classification and regression. Features are usually numeric, but structural features ...
Here are two companies that might be the top AI stocks to buy right now. 1. Arista Networks. When you hear about AI, it's often about companies rushing to buy the semiconductor chips necessary to ...
e. In natural language processing (NLP), a word embedding is a representation of a word. The embedding is used in text analysis. Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. [1]