Tips on how to Enhance AI Efficiency by Understanding Embedding High quality | by Eivind Kjosbakken | Feb, 2024

Learn to guarantee the standard of your embeddings, which could be important to your machine-learning system.

Creating high quality embeddings is a vital a part of most AI techniques. Embeddings are the muse on which an AI mannequin can do its job, and creating high-quality embeddings is, due to this fact, an necessary factor in making high-accuracy AI fashions. This text will speak about how one can guarantee the standard of your embeddings, which can assist you create higher AI fashions.

To begin with, embeddings are info saved as an array of numbers. That is usually required when you find yourself utilizing an AI mannequin, because the AI fashions solely settle for numbers as enter, and you can’t for instance feed textual content straight into an AI mannequin to do NLP evaluation. Creating embeddings could be accomplished with a number of totally different approaches like autoencoders or from coaching on downstream duties. The issue with embeddings nonetheless is that they’re meaningless to the human eye. You can not decide the standard of an embedding by merely trying on the numbers, and measuring the standard of the embeddings on the whole is usually a difficult process. Thus, this text will clarify how one can get a sign of the standard of your embedding, although these strategies sadly can’t assure the standard of the embeddings, contemplating it is a difficult process.

· Introduction
· Table of contents
· Dimensionality reduction
∘ Qualitative approach
∘ Quantitative approach
∘ When to use dimensionality reduction
∘ When not to use dimensionality reduction
· Embedding similarity
∘ When to use embedding similarity
∘ When not to use embedding similarity
· Downstream tasks
∘ When to use downstream tasks
∘ When not to use downstream tasks
· Improving your embeddings
∘ Open-source models
∘ Check for bugs
· Conclusion
· References