Archives for embeddings

27 Jan

Now, OpenAI API have text and code embeddings

OpenAI has introduced embeddings, a new endpoint in the OpenAI API, to assist in semantic search, clustering, topic modeling, and classification. OpenAI’s embeddings outperform top models in three standard benchmarks, including a 20% relative improvement in code search. Embeddings are really useful for working with natural language and code. The embeddings that are numerically similar […]

27 Jun

Guide To Word2vec Using Skip Gram Model

Vijaysinh Lendave cbow

In natural language processing, word embedding is a term used to represent words for text analysis,

The post Guide To Word2vec Using Skip Gram Model appeared first on Analytics India Magazine.

10 Apr

Guide to Pykg2vec: A Python Library for Knowledge Graph Embedding

Rajkumar Lakshmanamoorthy Ampligraph

Pykg2vec is a robust and powerful Python library for Knowledge Graph Embedding to represent Entity Relationships in different ML domains

The post Guide to Pykg2vec: A Python Library for Knowledge Graph Embedding appeared first on Analytics India Magazine.

10 Apr

Guide to Pykg2vec: A Python Library for Knowledge Graph Embedding

Rajkumar Lakshmanamoorthy Ampligraph

Pykg2vec is a robust and powerful Python library for Knowledge Graph Embedding to represent Entity Relationships in different ML domains

The post Guide to Pykg2vec: A Python Library for Knowledge Graph Embedding appeared first on Analytics India Magazine.

06 Mar

Guide to Sense2vec – Contextually Keyed Word Vectors for NLP

Pavan Kandru contextual text analysyis

Sense2vec is a neural network model that generates vector space representations of words from large corpora. It is an extension of the infamous word2vec algorithm.Sense2vec creates embeddings for ”senses” rather than tokens of words.

The post Guide to Sense2vec – Contextually Keyed Word Vectors for NLP appeared first on Analytics India Magazine.

23 Jan

Machine Learning Makes Amazon’s Alexa Smarter, Reduces Error Rate By 8%

Anirudh VK Alexa

Alexa, Amazon’s poster child for connected home devices, has recently received yet another update. Researchers have developed a method of implementing improved natural language processing in the model, cutting the error rate by 8%. This was done through a combination of transfer learning and utilising AI to generate ‘embeddings’ of words. Transfer learning was implemented…

The post Machine Learning Makes Amazon’s Alexa Smarter, Reduces Error Rate By 8% appeared first on Analytics India Magazine.