chore: import upstream snapshot with attribution

docs/EMBEDDINGS.md

@@ -0,0 +1,77 @@
+# 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)