In AI and machine learning projects, instead of directly processing raw data, it is necessary to make it more meaningful and processable. An important concept that comes into play at this point is Embedding. Embedding refers to the representation of data points as high-dimensional vectors. This method is widely used, especially in areas such as natural language processing (NLP) and computer vision (CV). In this article, we will explore what embedding is, how it works and its importance in AI projects.
Embedding is a mathematical transformation method that represents data as lower-dimensional, continuous vectors. This process allows raw data (e.g. words, images or items) to be meaningfully positioned in a high-dimensional space. Each data point is placed at a position in the vector space, and the distances or directions of these vectors represent meaningful relationships between the data.
For example, in the field of natural language processing, words are often transformed into vectors through so-called “word embedding”. Words with similar meanings are located close to each other in the vector space, while words with different meanings are located further away. Models such as GPT and Large Language Models (LLMs) use these embedding methods to process and make sense of text.
Embedding is a type of transformation that makes complex relationships between data more understandable and processable. By using these vector representations, machine learning algorithms can make more effective predictions on the data and optimize their learning process. We can explain how the embedding process works in the following steps:
Embedding can be used in various approaches according to different data types and application areas. Here are the most common types of embedding:
Embedding offers many advantages in machine learning and artificial intelligence projects:
Embedding has a wide range of uses in the world of artificial intelligence and machine learning. Here are the most common uses:
The use of embedding in machine learning and artificial intelligence projects is increasing. Especially in large language models and deep learning-based systems, the role of embedding in making sense of data is critical. Embedding is also expected to play an important role in new learning approaches such as Reinforcement Learning from Human Feedback (RLHF) and self-supervised learning.
Embedding is a powerful tool that transforms raw data into a more understandable and processable form. In machine learning and artificial intelligence projects, it enables models to work more effectively by revealing meaningful relationships between data. Widely used in both text and image processing, embedding is an essential method for achieving success in AI projects.
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