Machine learning algorithms have revolutionized the way we extract insights and make sense of vast amounts of data. One of the fundamental tasks in machine learning is measuring the similarity between objects, whether they are text documents, images, or any other form of data. Cosine similarity is a powerful technique that has emerged as a popular choice for quantifying similarity in machine learning applications. In this blog post, we will explore the concept of cosine similarity, its mathematical foundation, its applications in various domains, and how it can be implemented using TensorFlow framework. What is Cosine Similarity? Cosine similarity is a metric used to determine how similar two vectors are, regardless of their magnitude. It measures the cosine of the angle between two vectors projected into a multidimensional space. The resulting value ranges from -1 to 1, where 1 represents perfect similarity, 0 indicates no similarity, and -1 indicates perfect dissimilarity. The key ad