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Linear regression using gradient descent explained simply

Understand what is Linear Regression Gradient Descent in Machine Learning and how it is used. Linear Regression Gradient Descent is an algorithm we use to minimize the cost function value, so as to ...
Learn With Jay on MSN

Neural networks explained: Forward and backward propagation simplified

In this video, we will understand forward propagation and backward propagation. Forward propagation and backward propagation in Neural Networks, is a technique we use in machine learning to train our ...
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 ...
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 ...
Hacker

Why Gradient Descent Converges (and Sometimes Doesn’t) in Neural Networks

Amplifying words and ideas to separate the ordinary from the extraordinary, making the mundane majestic. Amplifying words and ideas to separate the ordinary from the extraordinary, making the mundane ...
Visual Studio Magazine

Kernel Ridge Regression with Stochastic Gradient Descent Training Using JavaScript

Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single numeric value. The demo uses stochastic gradient descent, one of two ...
eLife

Biologically informed cortical models predict optogenetic perturbations

Multi area RNN models fitted to in-vivo cortical activity predict behavioral changes induced by optogenetic perturbations, if biologically informed connectivity constraints on the optogenetically ...
IEEE

Subpixel target detection in hyperspectral imaging using a deep neural network with a variable stepsize gradient descent method

Abstract: The main difficulty in using artificial neural networks, which are designed for classification, to detect a rare subpixel target in hyperspectral imaging is that there is typically only one ...
IEEE

Regularized over-parametrized neural networks learned by gradient descent can generalize well

Abstract: Estimation of univariate regression function by a neural network with one hidden layer is considered, where the weight vector is determined by applying gradient descent to a regularized ...