I have a strong background in mathematics, particularly in calculus from high school, and I can confidently perform various matrix multiplications and operations by hand. This has given me a solid understanding of vectors from both mathematical and computer science perspectives.

However, I wasn’t fully aware of their geometric implications until recently. As I began exploring geometric explanations, I started perceiving lists, arrays, and embeddings as something pointing to a specific point in space. 🌌

Understanding the geometric perspective of vectors allows us to approach problems through the lens of geometry, deepening our comprehension of the underlying mathematics in machine learning. I highly recommend that anyone interested in machine learning, but not yet familiar with its geometric implications, explore the following courses:

I believe that investing time in this could potentially change the way you think. 💡