Embeddings are an innovative method of data representation, offering a unique way to visualise complex data sets. By mapping high-dimensional data onto lower-dimensional spaces, they enable the visualisation of data in a manner that can be easily understood.

Machine learning models, particularly those using neural networks, often use embeddings. These models convert categorical variables into continuous vectors, making them easier to process. An example of this is Word2Vec, an algorithm that creates word embeddings by training a neural network on a large corpus of text.

Embeddings can also be used to visualise the relationships between different items in a data set. This is achieved by using a dimensionality reduction technique such as t-SNE or UMAP, which map high-dimensional data into a two or three-dimensional space.

While embeddings offer many advantages, they are not without their limitations. The process of creating an embedding can be computationally intensive, and the resulting visualisations can sometimes be difficult to interpret. Despite these challenges, the use of embeddings is becoming increasingly popular in the field of data analysis and machine learning.

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