Optimization algorithm for artificial neural networks This article is about the computer algorithm. For the biological process, see Neural backpropagation. Backpropagation can also refer to the way ...

You can find java test/example programs in the test directory on Github. 👷‍♂️ TesterSimpleNumbers.java is the most simple example, training a one-hidden-layer backpropagation network to approximate a ...

Abstract: In this paper, we propose a novel backpropagation algorithm for delay-Doppler (DD) sensing using Zak transform-based orthogonal time frequency space (OTFS) modulation. We present the system ...

Hello! Following our previous session, we will continue to learn about the "Overview of Deep Learning," which is a requirement for G-Test preparation. Among these topics, our theme today is ...

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 ...

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: This study proposes theories and applications of probabilistic divergences to neural network training. This theory generalizes the cross-entropy method for backpropagation to the ...

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 ...

Language-based agentic systems represent a breakthrough in artificial intelligence, allowing for the automation of tasks such as question-answering, programming, and advanced problem-solving. These ...

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 ...