Learn With Jay on MSN

Backpropagation through time explained for RNNs

In this video, we will understand Backpropagation in RNN. It is also called Backpropagation through time, as here we are ...
Scientific Research Publishing

Ruder, S. (2016) An Overview of Gradient Descent Optimization Algorithms. arXiv Preprint.

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 ...
Scientific Research Publishing

Bengio, Y., Simard, P. and Frasconi, P. (1994) Learning Long-Term Dependencies with Gradient Descent Is Difficult. IEEE Transactions on Neural Networks, 5, 157-166.

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 ...
The New York Times

5 Steps to Solve (Almost) Any Problem

You probably don’t need more time. By Jancee Dunn When I look back on all the major decisions I’ve dithered over, I could scream. It took me a decade to commit to becoming a parent. I wavered for a ...
GitHub

Inquiry about the vanishing gradient problem

Hello, thank you very much for your outstanding contribution. I am truly interested in your work! While attempting to reproduce your results, I observed that the loss drops rapidly during the first ...
Semiconductor Engineering

Implementing AI Activation Functions

Activation functions play a critical role in AI inference, helping to ferret out nonlinear behaviors in AI models. This makes them an integral part of any neural network, but nonlinear functions can ...
marktechpost

University of South Florida Researchers Propose TeLU Activation Function for Fast and Stable Deep Learning

Inspired by the brain, neural networks are essential for recognizing images and processing language. These networks rely on activation functions, which enable them to learn complex patterns. However, ...
IEEE

Preprocessing based solution for the vanishing gradient problem in recurrent neural networks

Abstract: In this paper, a possible solution to the vanishing gradient problem in recurrent neural networks (RNN) is proposed. The main idea consists of pre-processing the signal (a time series ...