The selection of the optimization algorithm (optimizer) is one of the most essential
endeavors in Deep Learning and across all categories of Neural Networks. It's a matter of …

Adopting the most suitable optimization algorithm (optimizer) for a Neural Network Model is
among the most important ventures in Deep Learning and all classes of Neural Networks …

The role of optimizer in deep neural networks model impacts the accuracy of the model.
Deep learning comes under the umbrella of parametric approaches; however, it tries to relax …

The aim of the present study is to develop an algorithm by exploring the basic structure and
functional operation of convolution neural network's (CNN) design as well as the features of …

Deep learning is a sub-branch of artificial intelligence that acquires knowledge by training a
neural network. It has many applications in the field of banking, automobile industry …

In this paper, we perform a comparative evaluation of seven most commonly used first-order
stochastic gradient-based optimization techniques in a simple Convolutional Neural …

Training the deep learning models involves learning of the parameters to meet the objective
function. Typically the objective is to minimize the loss incurred during the learning process …

Abstract The Meta-heuristic and Heuristic algorithms that have been introduced for deep
neural network optimization is in this paper. Artificial Intelligence, and also the most used …

The twin issues of loss quality (accuracy) and training time are critical in choosing a
stochastic optimizer for training deep neural networks. Optimization methods for machine …

Machine learning has enormously contributed towards optimization techniques with new
ways for optimization algorithms. These approaches in deep learning have wide …