This article will show you different approaches you can take to create embeddings for your data
Creating quality embeddings from your data is crucial for your AI system’s efficacy. This article will show you different approaches you can use to convert your data from formats like images, texts, and audio, into powerful embeddings that can be used for your machine learning tasks. Your ability to create high-performance embeddings will have a large impact on the performance of your AI system, hence it is essential to learn and understand how to craft quality embeddings.
The motivation for this article is that creating good embeddings from your data is essential to most AI systems and it is therefore something you often have to do, making better embeddings a good way of improving all your future AI systems. The use cases for creating embeddings are tasks like clustering, similarity search, and anomaly detection, all of which can massively benefit from better embeddings. This article will explore two main ways of calculating embeddings; using an online model or training your very own model, which will both be discussed in subsequent sections of this article.
· Introduction
· Table of contents
· Motivation and use case
· Create embeddings using PyTorch models
· Create embeddings using HuggingFace models
∘ Approach 1
∘ Approach 2
· Create embeddings using GitHub
· Creating embeddings using paid models
· Create your own embeddings
∘ Autoencoders
∘ Training your own model on a downstream task
· Typical errors when creating embeddings
∘ Forget to use a pre-trained model
∘ License
· Conclusion
