Embeddings: A General Overview of Neural Network Representations
In the domain of neural networks, computers don’t comprehend words as humans do; they understand numbers. This fundamental distinction leads us to the concept of embeddings, a vital component in translating raw data into a format digestible by neural networks.
What are Embeddings?
Embeddings are numerical representations of data, typically in the form of vectors. These vectors, often referred to as input embeddings, are essentially matrices (n x 1) that serve as the gateway for data into neural networks. Their primary function is to convert raw data into a format that neural networks can process effectively.
Link to Word Embedding: Understanding the Fundamentals
Properties of the Embeddings
Capturing Semantic Meaning
One of the most interesting aspects of embeddings is their ability to capture the semantic meaning of data. Consider the words “King,” “Queen,” and “Car.” While humans inherently understand that “King” and “Queen” are more similar in meaning than either is to “Car,” neural networks require numeric representations to grasp this concept. By representing each word as a vector and plotting them in a multi-dimensional space called an embedding space, we provide the neural network with a framework to understand semantic relationships.
Continuous Dense Vectors
Embeddings are typically continuous dense vectors, meaning each vector component can take on any real number. Additionally, these vectors are dense, implying that most values within them are nonzero. This characteristic allows for distinct representations where similarity and proximity in the embedding space correspond to similarity and relationships in the original data.
Compressed Notation
In practical terms, embeddings offer a compressed notation for representing data. Consider the challenge of directly processing large datasets such as images or audio samples. The sheer size of these datasets can overwhelm computational resources. Embeddings provide a more manageable representation, condensing complex data into compact vectors without sacrificing essential information.
Applications of Embeddings
Embeddings find wide applications across various domains, from natural language processing (NLP) to computer vision. In NLP, embeddings play a crucial role in tasks like machine translation, where each word in a sentence is converted into vectors for processing. Similarly, in computer vision, embeddings are utilized to process images efficiently, enabling tasks like image classification.
Transformer Neural Networks
Transformer neural networks, renowned for their efficacy in sequence-to-sequence tasks, heavily rely on embeddings. By converting words into embeddings, transformers can effectively translate text from one language to another. These embeddings evolve across different layers of the network, maintaining their semantic representation throughout.
Vision Transformer
In computer vision, the Vision Transformer architecture employs embeddings to process images. The network can analyse and classify images effectively by breaking down images into smaller patches and converting them into embeddings. These embeddings encapsulate the semantic meaning of image patches, allowing for robust image understanding.
