We introduce a framework for training deep neural networks on clusters of computers with
the following appealing properties:(1) It is developed in Python, exposing an amiable …

Myriad of machine learning (ML) algorithms refine model parameters iteratively. Existing
synchronous data-parallel frameworks can accelerate training with convergence …

Big Data concerns with large-volume complex growing data. Given the fast development of
data storage and network, organizations are collecting large ever-growing datasets that can …

The practice of data science and machine learning often involves training many kinds of
models, for inferring some target variable, or extracting structured knowledge from data …

Salman et al, Mesopotamian Journal of Big Data Vol. (2023), 2023, 12–15 Page 1
Research Article Parallel Machine Learning Algorithms Saba Abdulbaqi Salman*1,, Saad …

The landscape of machine learning applications is changing rapidly: large centralized
datasets are replaced by high volume, high velocity data streams generated by a vast …

We present a novel approach for parallel computation in the context of machine learning that
we call" Tell Me Something New"(TMSN). This approach involves a set of independent …

Machine learning (ML) algorithms are commonly applied to big data, using distributed
systems that partition the data across machines and allow each machine to read and update …

Parallel Computing for Artificial Neural Network Training using Java Native Socket
Programming Page 1 Periodicals of Engineering and Natural Sciences Vol. 6, No. 1 …

Neural networks have become a cornerstone of machine learning. As the trend for these to
get more and more complex continues, so does the underlying hardware and software …