Embedding

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• AI Solver
• ...find outliers
• Dimensional Reduction
• AI-Powered Search

Types:

• Local Linear Embedding (LLE)
• T-Distributed Stochastic Neighbor Embedding (t-SNE)

Embedding...

• projecting an input into another more convenient representation space. For example we can project (embed) faces into a space in which face matching can be more reliable. | Chomba Bupe
• a mapping of a discrete — categorical — variable to a vector of continuous numbers. In the context of neural networks, embeddings are low-dimensional, learned continuous vector representations of discrete variables. Neural network embeddings are useful because they can reduce the dimensionality of categorical variables and meaningfully represent categories in the transformed space. Neural Network Embeddings Explained | Will Koehrsen - Towards Data Science
• a relatively low-dimensional space into which you can translate high-dimensional vectors. Embeddings make it easier to do machine learning on large inputs like sparse vectors representing words. Ideally, an embedding captures some of the semantics of the input by placing semantically similar inputs close together in the embedding space. An embedding can be learned and reused across models. Embeddings | Machine Learning Crash Course

Embeddings have 3 primary purposes:

1. Finding nearest neighbors in the embedding space. These can be used to make recommendations based on user interests or cluster categories.
2. As input to a machine learning model for a supervised task.
3. For visualization of concepts and relations between categories.

OpenAI Note

• New and improved embedding model ... We are excited to announce a new embedding model which is significantly more capable, cost effective, and simpler to use.
• [https://platform.openai.com/docs/guides/embeddings

Embeddings are a numerical representation of text that can be used to measure the relateness between two pieces of text. Our second generation embedding model, text-embedding-ada-002 is a designed to replace the previous 16 first-generation embedding models at a fraction of the cost. An embedding is a vector (list) of floating point numbers. The distance between two vectors measures their relatedness. Small distances suggest high relatedness and large distances suggest low relatedness.

OpenAI’s text embeddings measure the relatedness of text strings. Embeddings are commonly used for:

• Search (where results are ranked by relevance to a query string)
• Clustering (where text strings are grouped by similarity)
• Recommendations (where items with related text strings are recommended)
• Anomaly detection (where outliers with little relatedness are identified)
• Diversity measurement (where similarity distributions are analyzed)
• Classification (where text strings are classified by their most similar label)