id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_q6u36m2psnc3rpytggbjzexftu	Edward Meeds	MLitB: machine learning in the browser	2015	24	.pdf	application/pdf	11451	1976	-13	internet capable device to run training algorithms and predictive models with no and (3) design research closures, software objects that archive ML models, algorithms, and training a large class of ML models using distributed stochastic gradient descent (SGD). Reproducibility: MLitB should foster reproducible science with research closures, universally readable objects containing ML model specifications, algorithms, and parameters, The usage of web browsers as compute nodes provides the capability of running sophisticated ML algorithms without the expense and technical difficulty of using custom grid or as clients join/leave the network, client computations are received by the server, users add to the master server they use Web Workers to perform different tasks. The data server is a bespoke application intended to work with our neural network use-case The most performant deep neural network models are trained with sophisticated scientific Other distributed ML algorithm research includes the parameter server model (Li	./cache/work_q6u36m2psnc3rpytggbjzexftu.pdf	./txt/work_q6u36m2psnc3rpytggbjzexftu.txt