ABSTRACT: Artificial deep neural networks (ADNNs) have become a cornerstone of modern machine learning, but they are not immune to challenges. One of the most significant problems plaguing ADNNs is ...

Learn how gradient descent really works by building it step by step in Python. No libraries, no shortcuts—just pure math and code made simple. It Sits on a Vast Haul of Mineral Wealth. Now This Arctic ...

Introduction: We present Quantum Adaptive Search (QAGS), a hybrid quantum-classical algorithm for global optimization of multivariate functions. The method employs an adaptive mechanism that ...

Pull requests help you collaborate on code with other people. As pull requests are created, they’ll appear here in a searchable and filterable list. To get started, you should create a pull request.

This repository features a Linear Regression model built from scratch in Python, using gradient descent for parameter optimization. Includes scripts and Jupyter ...

Abstract: Convolutional neural network is the most important algorithm in the field of deep learning. The traditional convolution neural network usually uses Sigmoid or Relu as the activation function ...

Abstract: We develop an iterative numerical method for open-loop optimal control problems based on constrained-gradient descent in function space. The descent algorithm uses a projection of the ...