We will discuss word embeddings this week. Word embeddings represent a fundamental shift in natural language processing (NLP), transforming words into dense vector representations that capture ...

Word embeddings are directly responsible for many of the exponential advancements natural language technologies have made over the past couple years. They’re foundational to the functionality of ...

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In this video, we will learn about training word embeddings. To train word embeddings, we need to solve a fake problem. This problem is something that we do not care about. What we care about are the ...

Bilingual word embeddings (BWEs) play a very important role in many natural language processing (NLP) tasks, especially cross-lingual tasks such as machine translation (MT) and cross-language ...

In the realm of natural language processing (NLP), the concept of embeddings plays a pivotal role. It is a technique that converts words, sentences, or even entire documents into numerical vectors.

Vector similarity search uses machine learning to translate the similarity of text, images, or audio into a vector space, making search faster, more accurate, and more scalable. Suppose you wanted to ...