Recent hardware developments have dramatically increased the scale of data parallelism
available for neural network training. Among the simplest ways to harness next-generation …

Increasing the batch size is a popular way to speed up neural network training, but beyond
some critical batch size, larger batch sizes yield diminishing returns. In this work, we study …

Simple Summary Skin cancer is a major concern worldwide, and accurately identifying it is
crucial for effective treatment. we propose a modified deep learning model called …

Applying machine learning techniques to the quickly growing data in science and industry
requires highly-scalable algorithms. Large datasets are most commonly processed" data …

Due to the lack of data available for training, deep learning hardly performed well in the field
of garbage image classification. We choose the TrashNet data set which is widely used in …

Much research in recent years has focused on using empirical machine learning
approaches to extract useful insights on the structure-property relationships of …

It has been experimentally observed that the efficiency of distributed training with stochastic
gradient (SGD) depends decisively on the batch size and—in asynchronous …

Training Convolutional Neural Network (CNN) is a computationally intensive task, requiring
efficient parallelization to shorten the execution time. Considering the ever-increasing size of …

Momentum has become a crucial component in deep learning optimizers, necessitating a
comprehensive understanding of when and why it accelerates stochastic gradient descent …

Deep neural networks (DNNs) are typically optimized using various forms of mini-batch
gradient descent algorithm. A major motivation for mini-batch gradient descent is that with a …