=================================================================================

Embeddings are a technique used in machine learning and deep learning to represent data in a lower-dimensional space, preserving certain properties or relationships between the data points. The term "embedding" refers to the process of mapping high-dimensional data into a lower-dimensional space:

• Word Embeddings: Word embeddings are representations of words in a continuous vector space, where words with similar meanings are mapped to nearby points in the space. These representations capture semantic relationships between words. Popular methods for word embeddings include Word2Vec, GloVe, and fastText. 

• Image Embeddings: Image embeddings represent images in a lower-dimensional space. Convolutional Neural Networks (CNNs) are commonly used to extract features from images, and these features can be considered as embeddings. Techniques like transfer learning often involve using pre-trained models to extract image embeddings. 

• Sentence/Text Embeddings: Similar to word embeddings, sentence or text embeddings aim to represent entire sentences or paragraphs in a continuous vector space. Methods such as Doc2Vec, Universal Sentence Encoder, and BERT are used for creating sentence embeddings. 

• Knowledge Graph Embeddings: In knowledge representation, embeddings are used to represent entities and relationships in knowledge graphs. Methods like TransE, TransR, and DistMult are examples of knowledge graph embedding models. 

• Graph Embeddings: Graph embeddings represent nodes or entire graphs in a lower-dimensional space. These embeddings capture the structural information and relationships within a graph. Techniques like GraphSAGE, Node2Vec, and Graph Convolutional Networks (GCNs) are used for graph embeddings. 

• Audio Embeddings: Audio embeddings represent features extracted from audio signals in a lower-dimensional space. These can be used for tasks like speech recognition or audio classification. 

• Time Series Embeddings: Time series embeddings capture temporal patterns in sequential data. Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks are often employed for creating time series embeddings. 

• Event Embeddings: Embeddings can also be applied to represent events or occurrences in a lower-dimensional space. This is useful in event prediction or anomaly detection. 

SentenceTransformers, as shown in 2384, is a framework for state-of-the-art sentence, text and image embeddings in Python.

Figure 2384. SentenceTransformers.

============================================