stochastic-gradient-descent
There are 394 repositories under stochastic-gradient-descent topic.
Suji04/ML_from_Scratch
Implementation of basic ML algorithms from scratch in python...
je-suis-tm/machine-learning
Python machine learning applications in image processing, recommender system, matrix completion, netflix problem and algorithm implementations including Co-clustering, Funk SVD, SVD++, Non-negative Matrix Factorization, Koren Neighborhood Model, Koren Integrated Model, Dawid-Skene, Platt-Burges, Expectation Maximization, Factor Analysis, ISTA, FISTA, ADMM, Gaussian Mixture Model, OPTICS, DBSCAN, Random Forest, Decision Tree, Support Vector Machine, Independent Component Analysis, Latent Semantic Indexing, Principal Component Analysis, Singular Value Decomposition, K Nearest Neighbors, K Means, Naïve Bayes Mixture Model, Gaussian Discriminant Analysis, Newton Method, Coordinate Descent, Gradient Descent, Elastic Net Regression, Ridge Regression, Lasso Regression, Least Squares, Logistic Regression, Linear Regression
hiroyuki-kasai/SGDLibrary
MATLAB/Octave library for stochastic optimization algorithms: Version 1.0.20
gyrdym/ml_algo
Machine learning algorithms in Dart programming language
lixilinx/psgd_torch
Pytorch implementation of preconditioned stochastic gradient descent (affine group preconditioner, low-rank approximation preconditioner and more)
aditya9211/Blur-and-Clear-Classification
Classifying the Blur and Clear Images
hiroyuki-kasai/RSOpt
Riemannian stochastic optimization algorithms: Version 1.0.3
mynkpl1998/Deep-Learning-Optimization-Algorithms
Visualization of various deep learning optimization algorithms using PyTorch automatic differentiation and optimizers.
DylanMuir/fmin_adam
Matlab implementation of the Adam stochastic gradient descent optimisation algorithm
polyfem/polysolve
Easy-to-use linear and non-linear solver
mahdihosseini/RMSGD
Exploiting Explainable Metrics for Augmented SGD [CVPR2022]
ChunyuanLI/pSGLD
AAAI & CVPR 2016: Preconditioned Stochastic Gradient Langevin Dynamics (pSGLD)
qandeelabbassi/python-svm-sgd
Python implementation of stochastic sub-gradient descent algorithm for SVM from scratch
hiroyuki-kasai/OLSTEC
OnLine Low-rank Subspace tracking by TEnsor CP Decomposition in Matlab: Version 1.0.1
lixilinx/psgd_tf
Tensorflow implementation of preconditioned stochastic gradient descent
xcsf-dev/xcsf
XCSF learning classifier system: rule-based online evolutionary machine learning
sibirbil/SMB
Stochastic gradient descent with model building
harshraj11584/Paper-Implementation-Overview-Gradient-Descent-Optimization-Sebastian-Ruder
[Python] [arXiv/cs] Paper "An Overview of Gradient Descent Optimization Algorithms" by Sebastian Ruder
gcampanella/pydata-london-2018
Slides and notebooks for my tutorial at PyData London 2018
Vercaca/NN-Backpropagation
Implement a Neural Network trained with back propagation in Python
wajidarshad/LUPI-SVM
SVM with Learning Using Privileged Information (LUPI) framework
evarae/CNN_Tutorial
Hi! Thanks for checking out my tutorial where I walk you through the process of coding a convolutional neural network in java from scratch. After building a network for a university assignment, I decided to create a tutorial to (hopefully) help others do the same and improve my own understanding of neural networks.
rafi007akhtar/Digit-Classifier
Wrote a neural network that uses fundamental DL algorithms to identify handwritten digits from MNIST dataset.
bhattbhavesh91/gradient-descent-variants
My implementation of Batch, Stochastic & Mini-Batch Gradient Descent Algorithm using Python
HegdeChaitra/Yelp-Recommendation-System
Recommend Restaurants to User based on the ratings given by them to the restaurants
Cr4ckC4t/neural-network-from-scratch
A basic neural network with backpropagation programmed from scratch in C++
hiroyuki-kasai/SimpleDeepNetToolbox
Simple MATLAB toolbox for deep learning network: Version 1.0.3
chenpf1025/SLN
ICLR 2021: Noise against noise: stochastic label noise helps combat inherent label noise
kcg2015/DDPG_numpy_only
Implemenation of DDPG with numpy only (without Tensorflow)
mmahesh/variants-of-rmsprop-and-adagrad
SC-Adagrad, SC-RMSProp and RMSProp algorithms for training deep networks proposed in
hpca-uji/PyDTNN
PyDTNN - Python Distributed Training of Neural Networks
saadlabyad/aslsd
Parametric estimation of multivariate Hawkes processes with general kernels.
SSQ/Coursera-UW-Machine-Learning-Classification
Notebook for quick search
Adamdad/Filter-Gradient-Decent
In this paper, we propose Filter Gradient Decent (FGD), an efficient stochastic optimization algorithm that makes a consistent estimation of the local gradient by solving an adaptive filtering problem with different designs of filters.
ldv1/bbvi_spike_and_slab
Black-box spike and slab variational inference, example with linear models
ttungl/Deep-Learning
Implemented the deep learning techniques using Google Tensorflow that cover deep neural networks with a fully connected network using SGD and ReLUs; Regularization with a multi-layer neural network using ReLUs, L2-regularization, and dropout, to prevent overfitting; Convolutional Neural Networks (CNNs) with learning rate decay and dropout; and Recurrent Neural Networks (RNNs) for text and sequences with Long Short-Term Memory (LSTM) networks.