Slashing Deep Learning Speed With Hashing

An old trick Hashing taking advantage of tjr inherent sparsity in Big Data to reduce 90% time without loss of > 1% accuracy in data.

For examples : in picture recognition, many data are blanks consists of background (scenario,  lighting), only less than 10% are striking pattern data which characterise the particular objects, like zebra trait, tiger body skin lines, elephant trunk, sunflower… when search data are stored in a matrix of billion columns and rows, 90% elements are 0.