Gradient Descent is an algorithm very used by Machine Learning methods, as Recommender Systems in Collaborative Filtering. It tries to find the optimal values of some parameters in order to minimize a particular
In this paper, we introduce a new iterative algorithm for solving a generalized Sylvester matrix equation of the formwhich includes a class of linear matrix equations. The objective of the algorithm is to minimize an error at each iteration by the idea of gradient-descent. We show that the pr...
Adding supervised learning restrictive conditions to the negentropy objective function, we constrain the negentropy and restrictive conditions in a unified objective function and optimize the objective function by applying a new dual-gradient descent algorithm iteratively, which accelerates the computation ...
Definition Provides information about the rate of change of a function with respect to its input variables An optimization algorithm is used to minimize (or maximize) a function by iteratively moving in the direction of the negative gradient Usage Give insights into the function’s behavior and dir...
The training is performed using a defined set of rules, the learning algorithm. Training Algorithms Gradient Descent Algorithm—This is the simplest training algorithm used in a supervised training model. If the actual output is different from the target output, the difference or error is found....
It is the main purpose of this paper to investigate the impact of survey sampling with unequal inclusion probabilities on stochastic gradient descent-based M-estimation methods in large-scale statistical and machine-learning problems. Precisely, we prove that, in presence of some a priori information...
Structured illumination microscopy (SIM) has become the standard for next-generation wide-field microscopy, offering ultrahigh imaging speed, superresolution, a large field-of-view, and long-term imaging. Over the past decade, SIM hardware and software have flourished, leading to successful applicatio...
A novel hybrid deep fuzzy model based on gradient descent algorithm with application to time series forecasting Hui Zhang, Bo Sun, Wei Peng Article 121988 Article preview select article Linear approximation of the quantile–quantile plot for semantic labelling of numeric columns in tabular data Researc...
The exponential map \({{{\mathbf{X}}}_{1i}\) was then retrieved by computing the matrix logarithm of \({{{\mathbf{D}}}_1\), and employed as initial value for fitting a second order dispersion model, using gradient descent to directly optimize the exponential maps \({{{\mathbf{X}...
This repository is based upon thecourse materialby Stanford University. Professor Andrew Ng may not teach the most comprehensive lectures but he has inspired millions to study data science. This repository attempts to replicate every algorithm mentioned in the course as well as the popular ones out...