1 rich Libsyn https://www.libsyn.com 90 600 Try a walking desk to stay healthy while you study or work! Show notes at&amp;nbsp;ocdevel.com/mlg/32. L1/L2 norm, Manhattan, Euclidean, cosine distances, dot product Normed distances&amp;nbsp;link A norm is a function that assigns a strictly positive length to each vector in a vector space.&amp;nbsp;link Minkowski is generalized.&amp;nbsp;p_root(sum(xi-yi)^p). &quot;p&quot; = ? (1, 2, ..) for below. L1: Manhattan/city-block/taxicab.&amp;nbsp;abs(x2-x1)+abs(y2-y1). Grid-like distance (triangle legs). Preferred for high-dim space. L2: Euclidean.&amp;nbsp;sqrt((x2-x1)^2+(y2-y1)^2.&amp;nbsp;sqrt(dot-product). Straight-line distance; min distance (Pythagorean triangle edge) Others: Mahalanobis, Chebyshev (p=inf), etc Dot product A type of inner product. Outer-product: lies outside the involved planes. Inner-product: dot product lies inside the planes/axes involved&amp;nbsp;link. Dot product: inner product on a finite dimensional Euclidean space&amp;nbsp;link Cosine (normalized dot) Machine Learning Guide https://ocdevel.com/mlg <iframe title="Libsyn Player" style="border: none" src="//html5-player.libsyn.com/embed/episode/id/16722518/height/90/theme/custom/thumbnail/yes/direction/forward/render-playlist/no/custom-color/88AA3C/" height="90" width="600" scrolling="no" allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe> https://assets.libsyn.com/secure/item/16722518