numerical optimization in pytorch

A Python module to train simple multi-layer perceptron neural networks using Levenberg-Marquardt training

Calculate Camera Intrinsic and Extrinsic parameters using Zhang's Algorithm

梯度下降与Levenberg-Marquardt算法的比较。Comparison of gradient descent and Levenberg–Marquardt algorithm. Сравнение алгоритма градиентного спуска и алгоритма Левенберга-Марквардта.

Tight-binding parameter fitting package (TBFIT) for Slater-Koster method

collection of numerical optimization methods

Second order optimization with automatic differentiation

A MATLAB project to predict wind speed on the basis of temperature

Hybrid protocol FedLM-PSO that combines Particle Swarm Optimization (PSO) and Levenberg Marquardt (LM) to train MLP models in a Federated Learning environment

Levenberg-Marquardt Method is studied for finding the model parameters.

DQuickLTFit - A least-square fitting tool for the analysis of positron lifetime spectra using the Levenberg-Marquardt algorithm

Projects for Zhejiang University Applied Mathematics for Computer Science (1), implement some ML algorithms.

Implementation of trainlm in Matlab that uses the Levenberg_Marquardt backpropagation for training neural networks.

Implementation of a automatic Camera Calibration routine to find out intrinsic and extrinsic parameters with radial distortion

Implementation of a two-layer perceptron (from scratch) with four back-propagation methods in Python

This package implements some optimization methods used in numerical optimiation tasks for neural networks that aren't usually implemented in deep learning frameworks because of their computational constraints.

Documentación del trabajo de investigación de Tesis de Maestría - Luis Felipe Guevara Gómez

Implementing levenberg-marquardt algorithm from scratch without using any machine learning library

A project on understanding the performance of various different backpropagation optimization techniques in neural networks.

1st year master project: Projection of a 10-dimentional dataset into 2 or 3 dimentions using the Levenberg–Marquardt optimization algorithm, which was implemented.