quantization

Tensorflow Quantizaiton

On: October 8, 2019

In: AI, Computer Vision, Deep Machine Learning, NLP, Reinforcement Learning

During inference, precision in floats is not needed and can be reduced to using 8 bits instead of 32 bits this allows to bin continuous values to discrete ranges, and therefore is known as Quantization. This enables us to increase the bandwith sent (or increase the memory footprint in caseContinue Reading

K-Means Clustering

On: November 23, 2018

In: AI, Algorithms, Deep Machine Learning

k-means clustering is a method of vector quantization which finds good inter similarity between cluster members and intra-similarity between other clusters. k-means clustering partitions n observations into k clusters in which each observation belongs to the cluster with the nearest mean, These means are chosen so that in the nextContinue Reading