What Are Embeddings In Machine Learning?

Embeddings enable deep-learning models to understand real-world data domains more effectively. They simplify how real-world data is represented while retaining the semantic and syntactic relationships. This allows machine learning algorithms to extract and process complex data types and enable innovative AI applications. The following sections describe some important factors. 

Reduce data dimensionality

Data scientists use embeddings to represent high-dimensional data in a low-dimensional space. In data science, the term dimension typically refers to a feature or attribute of the data. Higher-dimensional data in AI refers to datasets with many features or attributes that define each data point. This can mean tens, hundreds, or even thousands of dimensions. For example, an image can be considered high-dimensional data because each pixel color value is a separate dimension.

When presented with high-dimensional data, deep-learning models require more computational power and time to learn, analyze, and infer accurately. Embeddings reduce the number of dimensions by identifying commonalities and patterns between various features. This consequently reduces the computing resources and time required to process raw data.

Train large language models

Embeddings improve data quality when training large language models (LLMs). For example, data scientists use embeddings to clean the training data from irregularities affecting model learning. ML engineers can also repurpose pre-trained models by adding new embeddings for transfer learning, which requires refining the foundational model with new datasets. With embeddings, engineers can fine-tune a model for custom datasets from the real world.

Build innovative applications

Embeddings enable new deep learning and generative artificial intelligence (generative AI) applications. Different embedding techniques applied in neural network architecture allow accurate AI models to be developed, trained, and deployed in various fields and applications. For example:

• With image embeddings, engineers can build high-precision computer vision applications for object detection, image recognition, and other visual-related tasks. 
• With word embeddings, natural language processing software can more accurately understand the context and relationships of words.
• Graph embeddings extract and categorize related information from interconnected nodes to support network analysis.

Computer vision models, AI chatbots, and AI recommender systems all use embeddings to complete complex tasks that mimic human intelligence.

Embeddings convert raw data into continuous values that ML models can interpret. Conventionally, ML models use one-hot encoding to map categorical variables into forms they can learn from. The encoding method divides each category into rows and columns and assigns them binary values. Consider the following categories of produce and their price.

Representing the values with one-hot encoding results in the following table.

The table is represented mathematically as vectors [1,0,0,5.00], [0,1,0,7.00], and [0,0,1,10.00].

One-hot encoding expands dimensional values of 0 and 1 without providing information that helps models relate the different objects. For example, the model cannot find similarities between apple and orange despite being fruits, nor can it differentiate orange and carrot as fruits and vegetables. As more categories are added to the list, the encoding results in sparsely distributed variables with many empty values that consume enormous memory space. 

Embeddings vectorize objects into a low-dimensional space by representing similarities between objects with numerical values. Neural network embeddings ensure that the number of dimensions remains manageable with expanding input features. Input features are traits of specific objects an ML algorithm is tasked to analyze. Dimensionality reduction allows embeddings to retain information that ML models use to find similarities and differences from input data. Data scientists can also visualize embeddings in a two-dimensional space to better understand the relationships of distributed objects. 

Engineers use neural networks to create embeddings. Neural networks consist of hidden neuron layers that make complex decisions iteratively. When creating embeddings, one of the hidden layers learns how to factorize input features into vectors. This occurs before feature processing layers. This process is supervised and guided by engineers with the following steps: 

1. Engineers feed the neural network with some vectorized samples prepared manually.
2. The neural network learns from the patterns discovered in the sample and uses the knowledge to make accurate predictions from unseen data.
3. Occasionally, engineers may need to fine-tune the model to ensure it distributes input features into the appropriate dimensional space. 
4. Over time, the embeddings operate independently, allowing the ML models to generate recommendations from the vectorized representations. 
5. Engineers continue to monitor the performance of the embedding and fine-tune with new data.