Machine Learning's Most Useful Multitool: Embeddings
Embeddings are one of the most versatile techniques in machine learning, and a critical tool every ML engineer should have in their tool belt. It’s a shame, then, that so few of us understand what they are and what they’re good for! The problem, maybe, is that embeddings sound slightly abstract and esoteric: In machine learning, an embedding is a way of representing data as points in n-dimensional space so that similar data points cluster together. Sound boring and unimpressive? Don’t be fooled. Because once you understand this ML multitool, you’ll be able to build everything from search engines to recommendation systems to chatbots, and a whole lot more. Plus, you don’t have to be a data scientist with ML expertise to use them, nor do you need a huge labeled dataset. Have I convinced you how neat these bad boys are?
Embeddings are one of the most versatile techniques in machine learning, and a critical tool every ML engineer should have in their tool belt. It’s a shame, then, that so few of us understand what they are and what they’re good for!
The problem, maybe, is that embeddings sound slightly abstract and esoteric:
In machine learning, an embedding is a way of representing data as points in n-dimensional space so that similar data points cluster together.
Sound boring and unimpressive? Don’t be fooled. Because once you understand this ML multitool, you’ll be able to build everything from search engines to recommendation systems to chatbots, and a whole lot more. Plus, you don’t have to be a data scientist with ML expertise to use them, nor do you need a huge labeled dataset.
Have I convinced you how neat these bad boys are?
