In a Nature Communications study, researchers from China have developed an error-aware probabilistic update (EaPU) method ...

Derive the Equations for the Backpropagation for Softmax and Multi-class Classification. In this video, we will see the equations for Backpropagation for Softmax and Multi-class Classification In the ...

Backpropagation in CNN is one of the very difficult concept to understand. And I have seen very few people actually producing content on this topic. So here in this video, we will understand ...

Deep neural networks (DNNs), which power modern artificial intelligence (AI) models, are machine learning systems that learn hidden patterns from various types of data, be it images, audio or text, to ...

A subtle but profound transformation is underway – one that’s redefining how people search and what content Google considers worth finding. We’ve all seen the headlines and the data: younger users are ...

ABSTRACT: This paper proposes a unique approach to load forecasting using a fast convergent artificial neural network (ANN) and is driven by the critical need for power system planning. The Mazoon ...

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

ABSTRACT: The stock market faces persistent challenges, including inefficiencies, volatility, and barriers to entry, which hinder its accessibility and reliability for investors. This paper explores ...

A new technical paper titled “The backpropagation algorithm implemented on spiking neuromorphic hardware” was published by University of Zurich, ETH Zurich, Los Alamos National Laboratory, Royal ...

Recent generations of machine learning, the methodology supporting artificial intelligence, have drawn inspiration from natural neural systems. These algorithmic approaches that mirror the complex ...