Learn how to implement SGD with momentum from scratch in Python—boost your optimization skills for deep learning.

Abstract: In the rapidly advancing Reinforcement Learning (RL) field, Multi-Agent Reinforcement Learning (MARL) has emerged as a key player in solving complex real-world challenges. A pivotal ...

Mini Batch Gradient Descent is an algorithm that helps to speed up learning while dealing with a large dataset. Instead of ...

Ayyoun is a staff writer who loves all things gaming and tech. His journey into the realm of gaming began with a PlayStation 1 but he chose PC as his platform of choice. With over 6 years of ...

Abstract: Distributed learning (DL) uses multiple nodes to accelerate training, enabling efficient optimization of large-scale models. Stochastic Gradient Descent (SGD), a key optimization algorithm, ...

Welcome to the Stochastic Control for Continuous Time Portfolios project! This application uses Deep Reinforcement Learning to help you manage your investments smartly. You will learn how to adapt ...

ABSTRACT: First, the necessary mathematical tools are recalled regarding the concepts of stochastic control processes, including stochastic optimal control and one of its fundamental principles, the ...

Stochastic gradient descent (SGD) provides a scalable way to compute parameter estimates in applications involving large-scale data or streaming data. As an alternative version, averaged implicit SGD ...

The first chapter of Neural Networks, Tricks of the Trade strongly advocates the stochastic back-propagation method to train neural networks. This is in fact an instance of a more general technique ...

Official implementation of the SAM-GS optimizer for multitask learning ArxIv Comparison of different MTL methods for 20000 steps.\ Top row: The loss trajectories of different MTL methods in the loss ...