GitHub

Stochastic Gradient Descent with Minibatch Line Search (in R).R

rm(list=ls()) #Cleans workspace. #Read in code. wdbc = read.csv('/Users/jennstarling/UTAustin/2016_Fall_SDS 385_Big_Data/Course Data/wdbc.csv', header=FALSE) y = wdbc ...
IEEE

Accelerating Minibatch Stochastic Gradient Descent Using Typicality Sampling

Abstract: Machine learning, especially deep neural networks, has developed rapidly in fields, including computer vision, speech recognition, and reinforcement learning. Although minibatch stochastic ...
IEEE

Accelerating Minibatch Stochastic Gradient Descent Using Typicality Sampling

Abstract: Machine learning, especially deep neural networks, has developed rapidly in fields, including computer vision, speech recognition, and reinforcement learning. Although minibatch stochastic ...
GitHub

d2l-en /chapter_optimization

At the heart of the decision to use minibatches is computational efficiency. This is most easily understood when considering parallelization to multiple GPUs and multiple servers. In this case we need ...