What Is an Embedding Model?

An embedding model is a machine learning tool used to transform complex, high-dimensional data into simpler, numerical values that machines can understand. This not only makes your data easier to process but also helps machine learning models uncover relationships and patterns. To more deeply understand this model, explore what embedding models are, how they work, and the different types you can use depending on your data structures.

What is an embedding model?

In machine learning, an embedding model is a tool you can use to simplify and structure different types of complex input data. Embedding models work by converting data such as words, images, audio, or graphs into lower-dimensional vector representations. These vectors are easier for machines to analyze and compare, allowing them to understand information in a way similar to humans. 

To review: A vector is a one-dimensional representation of data containing multiple values. A vector of the weather might be [2, 32, 1000]. This could represent whether it’s raining (2 = yes), the temperature (32 = 32 degrees Fahrenheit), and the location (1000 = a specific county). When it comes to data, the vector can capture different areas of relevant information, depending on what type of data you’re looking at.

For example, a computing algorithm might understand that 4 and 5 are closer together than 4 and 40. However, real-world data is not typically that simple. So, how do you help a machine understand that “red” and “yellow” are related terms, but “red” and “pink” are even more closely related? During training, machine learning models and artificial intelligence systems create “embeddings,” which are numerical values that simplify data representation while keeping the relationship between values the same. So if “red” has a value of 2.0, the algorithm might assign “yellow” to be 4.0 but “pink” to be 2.1. A full model will typically use a vector to represent different properties of each data point, allowing for more intricate modeling of relationships between different types of values. 

What types of objects are embedding models used for?

You can design embedding models for a range of data types, creating structured numerical representations of the input that your machine learning model can process. Common data types that you can embed include:

• Words: How words relate to each other (e.g., “king” and “queen”)

• Text: Encode entire bodies of text to understand meaning and relate written work

• Images: Allow for image classification and object detection

• Audio: Analyze music and sounds, including speech recognition

• Graphs: Assess networks (e.g., social media) to understand social connections or website links

What can you use embeddings for?

Embedding models reduce high-dimensional data into lower-dimensional representations while preserving relationships between data values, making it easier for machines to recognize and identify patterns and connections in your data. Embeddings are important for this in several ways, including:

Dimensionality reduction

Vectors can reduce complex features of data into a set of numerical values, making it easier to work with different types of information. This process of taking high-dimension data (such as images or video) and reducing it to numbers (low-dimensional data) is called dimensionality reduction. 

Model generalization

Embeddings help your machine learning models generalize to new data, which in turn helps the model make better and more accurate predictions. By using embeddings, your models are less likely to overfit to training data (i.e., be too specific to training data and unable to generalize) because the embeddings encode semantic relationships that remain consistent across different data sets. For example, word embeddings allow natural language processing algorithms to recognize relationships between new words that didn’t appear during training, helping models more accurately generalize to and understand new information.

Training neural networks

Neural networks use embeddings to process information more effectively, enabling deep learning models to interpret data and generalize with greater precision. You can use custom embeddings for domain-specific representations or pre-trained embeddings to accelerate the learning process. This is relevant for image embeddings in computer vision applications, word embeddings for natural language processing, and graph embeddings for network analysis.

Types of embedding models to explore

Depending on your data types, you can choose an embedding model that makes sense for your data. You can use general statistics analysis methods, such as principal component analysis, or pre-trained models designed for certain types of data. Types you might explore include:

Principal component analysis 

One form of dimensionality reduction is principal component analysis (PCA), which compresses similar data values into a representative information vector (such as combining “chihuahua,” “collie,” and “cocker spaniel” into a “dog” vector) to reduce the volume of information the machine has to process. This allows for more efficient data processing without losing relevant information. 

Word2Vec

This pre-built machine learning algorithm analyzes the relationship between words and represents them as embedded objects. This allows machines to understand language and predict what the user might say next. If you’re working with natural language processing, you might use Word2Vec to understand the sentiment of written phrases, translate between languages, or power certain types of recommendation engines. 

BERT

BERT is a pre-trained text embedder that can represent entire sentences and documents as vectors. This goes beyond individual word processing and can help understand user questions and retrieve specific information. BERT was pre-trained on data from Wikipedia and Google News, giving it practice in analyzing the meaning of large bodies of text.

ResNet

ResNet, or residual neural network, is a pre-training image embedding algorithm that can identify visual information in photos. This allows the algorithm to detect specific objects, identify similarities between objects, and recognize certain information in images. This is a deep learning framework that can powerfully map images, making it common for computer vision tasks. 

Who uses embeddings?

Data science professionals who work with computer vision, natural language processing, recommendation systems, or other machine learning models that require dimensionality reduction may use embedding models. 

Embeddings help data scientists represent features of their data by identifying commonalities between each data point. This helps to reduce computing resources that data scientists need to use to analyze their data and can help clean training data to reduce irregularities and improve model performance before running the new information through the algorithm.

How to build an embedding model

You can use embedding for a variety of tasks, each of which will have a slightly different flow. A common use case is product recommendation, helping businesses tailor their advertisements and campaigns to users who are most likely to buy a certain item. If you were creating a recommendation system, the embedding process might work as follows:

1. Input your data. Your machine learning model assesses information about consumer preferences, such as past purchases, clicks on different items, and categories of interest.

2. Create the embeddings. The system uses a pre-defined embedding model to assign values to each user representing their characteristics, such as their behavior and demographics. Each product would also have a vector, representing its niche and features.

3. Identify similarities. The algorithm identifies product vectors that most closely match consumer vectors, recommending products that align with customer preferences.

Learn more about machine learning on Coursera

Embedding models help machine learning models analyze complex data by translating it into information that is easier for machines to process. To learn more about embedding and how it fits into broader machine learning and computer science concepts, you can take exciting degree options on Coursera, offered by leading universities. For a more general overview of computer science concepts, consider the Bachelor of Science in Computer Science from the University of London or, for a more advanced degree, the Master of Science in Data Science from the University of Colorado Boulder.