Recently I’ve been interested in some of the ways that we can use embeddings to compare images, text, and other data. Embeddings allow us to create features and applications like image search, retrieval-augmented generation, and many more. You can think of an embedding as just a list of numbers (a vector) that represents the semantic meaning of a given piece of data. this allows us to compare two embeddings and determine how similar they are....
As I continue to work through chapters of the Practical Deep Learning course, my game plan is to write blog posts to solidify my learning of the content. Next on the list is taking our drink classifier we built in the first chapter and deploy it to production. There are many ways to go about doing this, but in this blog we’ll deploy to Hugging Face with Gradio. Let’s do this 🥂....
I recently started a new course called Practical Deep Learning. The course is taught by Jeremy Howard, a machine learning legend and founder of fast.ai. One of the first lessons in the course was training a classifier to detect birds in images. As part of learning the materials, I thought i’d be a fun experiment to extend this model to classify the top 10 most popular cocktails. Let’s get started 🍸🍹....