This blog post discusses image embeddings and its implementation in Python. I hope you find it useful and informative. Introduction Image embeddings are numerical representations of images that capture their semantic meaning and visual features. They are useful for many applications, such as image search, image classification, image retrieval, and image similarity. In this blog post, we will learn what image embeddings are, why they are important, and how to generate them using Python and some open-source libraries. Source What are image embeddings? An image embedding is a vector of numbers that represents an image in a high-dimensional space. For example, an image of a cat can be embedded as a vector of 384 numbers, such as [0.12, -0.34, ..., 0.05]. Each number in the vector corresponds to a feature or an attribute of the image, such as color, shape, texture, etc. The vector captures the essence of the image and allows us to compare it with other images using mathematical operations.