# Embeddings

Embeddings are a powerful tool for software developers working with artificial intelligence
and natural language processing. They allow computers to understand the meaning of
words in a more sophisticated way, by representing them as high-dimensional vectors
rather than simple strings of characters.

Embeddings work by mapping each word in a vocabulary to a point in a high-dimensional
space. This space is designed so that words with similar meanings are located near each other.
This allows algorithms to identify relationships between words, such as synonyms or
antonyms, without needing explicit rules or human supervision.

One popular method for creating embeddings is
Word2Vec [[1]](https://arxiv.org/abs/1301.3781)[[2]](https://arxiv.org/abs/1310.4546),
which uses neural networks to learn the relationships between words from large amounts
of text data. Other methods include GloVe and
[FastText](https://research.facebook.com/downloads/fasttext/). These methods
all have different strengths and weaknesses, but they share the common goal of creating
meaningful representations of words that can be used in machine learning models.

Embeddings can be used in many different applications, including sentiment analysis,
document classification, and recommendation systems. They are particularly useful
when working with unstructured text data where traditional methods like bag-of-words
models struggle, and are a fundamental part of **SK Semantic Memory**.

**Semantic Memory** is similar to how the human brain stores and retrieves knowledge about
the world. Embeddings are used to create a semantic memory by **representing concepts
or entities as vectors in a high-dimensional space**. This approach allows the model
to learn relationships between concepts and make inferences based on similarity or
distance between vector representations. For example, the Semantic Memory can be
trained to understand that "Word" and "Excel" are related concepts because they are
both document types and both Microsoft products, even though they use different
file formats and provide different features. This type of memory is useful in
many applications, including question-answering systems, natural language understanding,
and knowledge graphs.

Software developers can use pre-trained embedding model, or train their one with their
own custom datasets. Pre-trained embedding models have been trained on large amounts
of data and can be used out-of-the-box for many applications. Custom embedding models
may be necessary when working with specialized vocabularies or domain-specific language.

Overall, embeddings are an essential tool for software developers working with AI
and natural language processing. They provide a powerful way to represent and understand
the meaning of words in a computationally efficient manner.

## Applications

Some examples about embeddings applications.

1. Semantic Memory: Embeddings can be used to create a semantic memory, by which
   a machine can learn to understand the meanings of words and sentences and can
   understand the relationships between them.

2. Natural Language Processing (NLP): Embeddings can be used to represent words or
   sentences in NLP tasks such as sentiment analysis, named entity recognition, and
   text classification.

3. Recommender systems: Embeddings can be used to represent the items in a recommender
   system, allowing for more accurate recommendations based on similarity between items.

4. Image recognition: Embeddings can be used to represent images in computer vision
   tasks such as object detection and image classification.

5. Anomaly detection: Embeddings can be used to represent data points in high-dimensional
   datasets, making it easier to identify outliers or anomalous data points.

6. Graph analysis: Embeddings can be used to represent nodes in a graph, allowing
   for more efficient graph analysis and visualization.

7. Personalization: Embeddings can be used to represent users in personalized recommendation
   systems or personalized search engines.

## Vector Operations used with Embeddings

 - [Cosine Similarity](COSINE_SIMILARITY.md)
 - [Dot Product](DOT_PRODUCT.md)
 - [Euclidean Distance](EUCLIDEAN_DISTANCE.md)
