Image Similarity Search Using VGG16 and Cosine Distance - Tutorial 348

Learn to implement image similarity search in Python through a detailed 22-minute tutorial that demonstrates using VGG16 as a feature extractor combined with cosine distance metrics. Master the fundamentals of cosine similarity for vector comparison, understanding how it measures the alignment between two vectors with results ranging from -1 (opposite) to 1 (identical). Follow along with practical code examples that show how to compare query vectors against database vectors, with complete source code available on GitHub. Explore how cosine similarity calculates the cosine of angles between vectors using the formula cos(θ) = (A·B)/(||A||·||B||) and understand when vectors are perpendicular (similarity = 0).