Understand the Maths behind Backpropagation in Neural Networks. In this video, we will derive the equations for the Back Propagation in Neural Networks. In this video, we are using using binary ...

Abstract: There are a number of problems associated with training neural networks with backpropagation algorithm. The algorithm scales exponentially with increased complexity of the problem. It is ...

ABSTRACT: This paper proposes to apply the genetic algorithm and the firefly algorithm to enhance the estimation of the direction of arrival (DOA) angle of electromagnetic signals of a smart antenna ...

Bubble sort is one of the most intuitive sorting algorithms and a perfect starting point for anyone interested in the world of algorithms. Despite its simplicity, bubble sort provides a clear example ...

A TensorFlow-inspired neural network library built from scratch in C# 7.3 for .NET Standard 2.0, with GPU support through cuDNN ...

Emerging two-terminal nanoscale memory devices, known as memristors, have demonstrated great potential for implementing energy-efficient neuro-inspired computing architectures over the past decade. As ...

The backpropagation Algorithm is broadly used in machine learning. This algorithm is greatly used for training feed-forward neural networks. It permits the information from the cost to then flow ...

How can we train spiking neural networks to achieve brain-like performance in machine learning tasks? The resounding success and pervasive use of the backpropagation algorithm in deep learning ...

Abstract: A general backpropagation algorithm is proposed for feedforward neural network learning with time varying inputs. The Lyapunov function approach is used to ...

Deep learning is a form of machine learning that models patterns in data as complex, multi-layered networks. Because deep learning is the most general way to model a problem, it has the potential to ...