GitHub topics: stochastic-gradient-descent

Repositories

privateboss0/Artificial_Intelligence_Stanford

Stanford-CS221 class practical's, Assignments and projects

Language: Python - Size: 724 KB - Last synced at: about 17 hours ago - Pushed at: 1 day ago - Stars: 0 - Forks: 0

Jamie1377/STA410

A statistical computations and ML orientated Python package to predict stock price.

Language: Jupyter Notebook - Size: 33.6 MB - Last synced at: 4 days ago - Pushed at: 4 days ago - Stars: 0 - Forks: 1

# 🔍 Semantic Article RecommenderThis project offers a simple way to find articles that are similar in meaning. It uses advanced techniques like Hugging Face embeddings and FAISS for efficient searching. 🛠️

Language: Python - Size: 513 KB - Last synced at: 4 days ago - Pushed at: 4 days ago - Stars: 0 - Forks: 0

gyrdym/ml_algo

Machine learning algorithms in Dart programming language

Language: Dart - Size: 9.5 MB - Last synced at: 9 days ago - Pushed at: 9 days ago - Stars: 193 - Forks: 33

qzhao19/sgdlib

A header-only C++ Library for Optimization Algorithms

Language: C++ - Size: 804 KB - Last synced at: 18 days ago - Pushed at: 18 days ago - Stars: 4 - Forks: 0

Lionzap/ML-From-Scratch

Explore ML-From-Scratch for clear Python implementations of key machine learning models. Understand algorithms without complexity. 🐍🌟

Language: Python - Size: 468 KB - Last synced at: 19 days ago - Pushed at: 19 days ago - Stars: 0 - Forks: 0

pyrddlgym-project/pyRDDLGym-jax

JAX compilation of RDDL description files, and a differentiable planner in JAX.

Language: Python - Size: 34.5 MB - Last synced at: 17 days ago - Pushed at: 17 days ago - Stars: 7 - Forks: 1

kartikey2807/Differentially-Private-SGD

Implement DP-SGD using PyTorch (opacus library); accuracy decreases.

Language: Jupyter Notebook - Size: 22.5 MB - Last synced at: 30 days ago - Pushed at: 30 days ago - Stars: 0 - Forks: 0

xcsf-dev/xcsf

XCSF learning classifier system: rule-based online evolutionary machine learning

Language: C - Size: 49 MB - Last synced at: 16 days ago - Pushed at: 16 days ago - Stars: 35 - Forks: 13

DanielCortild/PEP-for-SGD-without-Variance

Code implementing the Performance Estimation Problem (PEP) methodology for SGD without variance assumption.

Language: Jupyter Notebook - Size: 453 KB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 0 - Forks: 0

lixilinx/psgd_torch

Pytorch implementation of preconditioned stochastic gradient descent (Kron and affine preconditioner, low-rank approximation preconditioner and more)

Language: Python - Size: 3.08 MB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 175 - Forks: 11

uds-helms/BEclear

Correction of batch effects in DNA methylation data

Language: R - Size: 1.07 MB - Last synced at: 23 days ago - Pushed at: over 2 years ago - Stars: 5 - Forks: 0

Suji04/ML_from_Scratch

Implementation of basic ML algorithms from scratch in python...

Language: Jupyter Notebook - Size: 540 KB - Last synced at: about 1 month ago - Pushed at: over 4 years ago - Stars: 300 - Forks: 231

subhaskghosh/hyperbolic_gar

Byzantine robust distributed stochastic gradient descent

Language: Python - Size: 56.5 MB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 0 - Forks: 0

x0rw/stochastic-gradient-descent-neural-network-in-C

Neural Network written in pure C, leveraging Stochastic Gradient Descent (SGD) for optimization. Designed for performance and efficiency, it avoids external dependencies, making it a lightweight yet powerful tool for understanding and experimenting with neural networks at a low level.

Language: C - Size: 74.2 KB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 1 - Forks: 0

kinit-sk/overshoot

Overshoot: Taking advantage of future gradients in momentum-based stochastic optimization

Language: Python - Size: 488 KB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 2 - Forks: 0

dmazilkin/NN-Perceptron-from-scratch

Perceptron realization from scratch for solving binary classification problem.

Language: Jupyter Notebook - Size: 78.1 KB - Last synced at: 2 months ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

YousefAlaaAli/Gradient-descent

A practical comparison of gradient descent algorithms to predict student performance using study and lifestyle data, with visual analysis.

Language: Jupyter Notebook - Size: 556 KB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

polyfem/polysolve

Easy-to-use linear and non-linear solver

Language: C++ - Size: 773 KB - Last synced at: 22 days ago - Pushed at: 22 days ago - Stars: 51 - Forks: 19

douglaskaue/gradient-cursor

A JavaScript library that applies a dynamic and customizable gradient cursor effect to enhance user interaction on web pages.

Language: JavaScript - Size: 13.7 KB - Last synced at: 4 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

gcampanella/pydata-london-2018

Slides and notebooks for my tutorial at PyData London 2018

Language: Jupyter Notebook - Size: 2.72 MB - Last synced at: about 1 month ago - Pushed at: about 7 years ago - Stars: 21 - Forks: 6

magnushelliesen/neural-network

Homemade neural network-class with a train/backpropagation method.

Language: Jupyter Notebook - Size: 86 MB - Last synced at: 4 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

LKEthridge/Numerical_Methods

A Numerical Methods project from TripleTen

Language: Jupyter Notebook - Size: 87.9 KB - Last synced at: 4 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

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

Language: Jupyter Notebook - Size: 7.84 MB - Last synced at: 3 months ago - Pushed at: over 2 years ago - Stars: 234 - Forks: 51

Hemanthsp999/LinRegC

This C library provides efficient implementations of linear regression algorithms, including support for stochastic gradient descent (SGD) and data normalization techniques. It is designed for easy integration into your C projects, enabling you to perform regression analysis on various datasets.

Language: C - Size: 330 KB - Last synced at: 5 months ago - Pushed at: 5 months ago - Stars: 0 - Forks: 0

natanaelalmeida/matrix-factorization

Understand how Matrix Factorization and SGD are used in personalized recommendation systems.

Language: Jupyter Notebook - Size: 28.3 KB - Last synced at: about 1 month ago - Pushed at: 5 months ago - Stars: 0 - Forks: 0

bhattbhavesh91/gradient-descent-variants

My implementation of Batch, Stochastic & Mini-Batch Gradient Descent Algorithm using Python

Language: Jupyter Notebook - Size: 115 KB - Last synced at: 19 days ago - Pushed at: almost 6 years ago - Stars: 18 - Forks: 21

pngo1997/Neural-Network-Backpropagation-Optimization

Explores key concepts in Neural Network training.

Language: Jupyter Notebook - Size: 21.5 KB - Last synced at: 5 months ago - Pushed at: 6 months ago - Stars: 0 - Forks: 0

pngo1997/Multiple-Regression-and-Feature-Selection-Analysis

Explores multiple linear regression, feature selection, Ridge & Lasso regression, and Stochastic Gradient Descent (SGD) regression.

Language: Jupyter Notebook - Size: 1.26 MB - Last synced at: 5 months ago - Pushed at: 6 months ago - Stars: 0 - Forks: 0

MoinDalvs/Gradient_Descent_For_beginners

Gradient_descent_Complete_In_Depth_for beginners

Language: Jupyter Notebook - Size: 1.36 MB - Last synced at: about 1 month ago - Pushed at: almost 3 years ago - Stars: 3 - Forks: 1

wongpc0817/SGD_demonstration

This project provides an interactive GUI to demonstrate the training process of neural networks using various Stochastic Gradient Descent (SGD) algorithms. Users can experiment with training and testing models, visualize results, and understand key performance metrics. The project aims to educate beginners and researchers about network training.

Language: MATLAB - Size: 68.6 MB - Last synced at: 4 months ago - Pushed at: 6 months ago - Stars: 0 - Forks: 0

12danielLL/Neural_Networks_Project

The project focuses on analyzing neural activity data to classify neuron types (spiny and aspiny). It integrates unsupervised learning methods (PCA, Autoencoders) and supervised learning models (Logistic Regression, MLP) to build accurate classifiers that effectively analyze neurons' electrical responses.

Language: Jupyter Notebook - Size: 2.93 MB - Last synced at: 4 months ago - Pushed at: 6 months ago - Stars: 0 - Forks: 0

Khushi130404/Gradient_Realm

Gradient_Realm is a Python project exploring regression techniques and optimization methods like Regularization, Batch, Stochastic, and Mini-batch Gradient Descent. It uses scikit-learn and custom implementations for hands-on learning.

Language: HTML - Size: 5.58 MB - Last synced at: 5 months ago - Pushed at: 7 months ago - Stars: 0 - Forks: 0

abisliouk/IE675b-machine-learning

This is a repository with the assignments of IE675b Machine Learning course at University of Mannheim.

Language: Jupyter Notebook - Size: 60.8 MB - Last synced at: 7 months ago - Pushed at: 7 months ago - Stars: 0 - Forks: 0

DylanMuir/fmin_adam

Matlab implementation of the Adam stochastic gradient descent optimisation algorithm

Language: Matlab - Size: 109 KB - Last synced at: 3 months ago - Pushed at: over 8 years ago - Stars: 56 - Forks: 24

Melvin-klein/algosto

A Python package that includes various stochastic algorithms.

Language: Python - Size: 2.38 MB - Last synced at: about 1 month ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

AnnettaQi/Spam-detection

Using Stochastic gradient descent to classify emails into spam or ham

Language: Jupyter Notebook - Size: 80.1 KB - Last synced at: 3 months ago - Pushed at: 9 months ago - Stars: 1 - Forks: 0

Vaishnavi8507/Pneumonia_Detection

Pneumonia Detection using Deep Learning where a standard labelled dataset is used for detecting viral and bacterial pneumonia

Language: Jupyter Notebook - Size: 2.72 MB - Last synced at: 8 months ago - Pushed at: 8 months ago - Stars: 0 - Forks: 0

martn2023/handwritten-number-classification-ML

End-to-end ML: classifying pictures of handwritten numbers with Random Forest

Language: Jupyter Notebook - Size: 2.18 MB - Last synced at: 4 months ago - Pushed at: 9 months ago - Stars: 0 - Forks: 0

vtramo/neural-networks-experiments-from-scratch

The objective of this repository is to provide a learning and experimentation environment to better understand the details and fundamental concepts of neural networks by building neural networks from scratch.

Language: Python - Size: 12.2 MB - Last synced at: about 2 months ago - Pushed at: almost 2 years ago - Stars: 1 - Forks: 0

DeepraMazumder/Social-Network-Ads-Prediction-Analysis

A Machine Learning project to predict user interactions with social network ads using demographic data to optimize ad targeting

Language: Jupyter Notebook - Size: 633 KB - Last synced at: 5 months ago - Pushed at: 10 months ago - Stars: 1 - Forks: 0

shreyansh26/Optimal-Bidding

This repository contains code for Optimal Bidding task of Inter IIT Tech Meet 2018. :chart_with_upwards_trend:

Language: Jupyter Notebook - Size: 12 MB - Last synced at: 5 months ago - Pushed at: over 7 years ago - Stars: 2 - Forks: 0

WilliamZhang20/Linear-Regression

Writing a linear regression algorithm from scratch

Language: Python - Size: 10.7 KB - Last synced at: 4 months ago - Pushed at: 11 months ago - Stars: 0 - Forks: 0

InCogNiTo124/recursive-sgd 📦

A proof of concept of a recursion doing stochastic gradient descent for a simple neural network. Done in Python3 with numpy

Language: Python - Size: 192 KB - Last synced at: 28 days ago - Pushed at: 11 months ago - Stars: 1 - Forks: 0

bhak90-tesh/Optimization

This is a Research of Various Optimization Algorithms that are used in ML and DL which is implemented on the 2 types of Dataset(Banglore_Housing & TSP)

Language: Jupyter Notebook - Size: 0 Bytes - Last synced at: 11 months ago - Pushed at: 11 months ago - Stars: 0 - Forks: 0

jo-valer/ASGD-optimizer

Averaged Stochastic Gradient Descent for Pytorch

Language: Python - Size: 68.4 KB - Last synced at: 11 months ago - Pushed at: 11 months ago - Stars: 1 - Forks: 0

fatemafaria142/Classification-of-Potato-Disease-A-Hybrid-Deep-Learning-Framework

This research presents a hybrid deep learning framework combining MobileNet V2 with LSTM, GRU, and Bidirectional LSTM for classifying various potato diseases. The study explores the performance of different architectures to determine the optimal configuration for accurate disease categorization.

Language: Jupyter Notebook - Size: 664 KB - Last synced at: 11 months ago - Pushed at: 11 months ago - Stars: 1 - Forks: 1

AmbreenMahhoor/What-Is-Gradient-Descent-And-Its-Variants

Language: Jupyter Notebook - Size: 277 KB - Last synced at: 12 months ago - Pushed at: 12 months ago - Stars: 0 - Forks: 0

mattsankner/micrograd

I build the Micrograd autogradient engine, which is a functioning neural network with forward pass, backward propagation, and stochastic gradient descent, all built from scratch. This is derived from the great @karpathy micrograd lecture. Each notebook is complete with Andrei's lecture code and speech, as well as my own code, anecdotes and addition

Language: Jupyter Notebook - Size: 6.35 MB - Last synced at: 12 months ago - Pushed at: 12 months ago - Stars: 1 - Forks: 0

AFLProjects/deepl

A simple deep learning library for training end-to-end fully-connected Artificial Neural Networks (ANNs), primarily based on numpy and autograd.

Language: Python - Size: 868 KB - Last synced at: 12 months ago - Pushed at: 12 months ago - Stars: 1 - Forks: 0

prajwalp3/Cardiovascular-Disease-prediction-using-Machine-learning-models

Our project utilizes machine learning models to predict cardiovascular diseases (CVDs) by analyzing diverse datasets and exploring 14 different algorithms. The aim is to enable early detection, personalized interventions, and improved healthcare outcomes.

Language: Jupyter Notebook - Size: 575 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

SGDinference-Lab/SGDinference

R package for SGD inference

Language: R - Size: 34.6 MB - Last synced at: 20 days ago - Pushed at: over 1 year ago - Stars: 1 - Forks: 1

LukasFiala0/Gradient_descents

Optimalization – finding parameters of linear regression using various algorithms

Language: Python - Size: 315 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

somefunAgba/autosgm

AutoSGM

Language: Python - Size: 4.99 MB - Last synced at: 9 months ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

lucaslopes/lp-learner

Dynamically adjusts load balancers coupled with auto scalers in response to workload changes using weakly coupled Markov Decision Processes (MDPs) and a two-timescale online learning approach.

Size: 33.2 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

Nico-Curti/rSGD

Replicated Stochastic Gradient Descent algorithm

Language: C++ - Size: 262 KB - Last synced at: about 2 months ago - Pushed at: almost 6 years ago - Stars: 3 - Forks: 1

zwd2016/AdaHMG

AdaHMG: A first-order stochastic optimization algorithm for time series data

Language: Python - Size: 3.54 MB - Last synced at: about 1 year ago - Pushed at: over 6 years ago - Stars: 8 - Forks: 2

harshraj11584/Paper-Implementation-Overview-Gradient-Descent-Optimization-Sebastian-Ruder

[Python] [arXiv/cs] Paper "An Overview of Gradient Descent Optimization Algorithms" by Sebastian Ruder

Language: Python - Size: 58.6 KB - Last synced at: about 1 year ago - Pushed at: over 6 years ago - Stars: 22 - Forks: 6

TianzhengHou/Artificial-Intelligence

This project will cover some of the basic Artificial Intelligence along the course using Python. Mainly will use Numpy to build everything. I write all the files in Python and it refers back to the school labs at Dalhousie University.

Language: Jupyter Notebook - Size: 70.3 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

spaceshark123/NeuralNetwork

flexible and extensible implementation of a multithreaded feedforward neural network in Java including popular optimizers, wrapped up in a console user interface

Language: Java - Size: 96.7 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 1 - Forks: 0

sueszli/get-rect

recommender systems algorithms

Language: HTML - Size: 1.83 MB - Last synced at: 9 months ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

mahdihosseini/RMSGD

Exploiting Explainable Metrics for Augmented SGD [CVPR2022]

Language: Python - Size: 19.5 MB - Last synced at: 3 months ago - Pushed at: over 3 years ago - Stars: 45 - Forks: 13

whdhdyt21/News_Article_Category_Classification

Language: Jupyter Notebook - Size: 9.59 MB - Last synced at: 3 months ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

astonglen/AirBnb-Price-Prediction

The ability to predict prices and features affecting the appraisal of property can be a powerful tool in such a cash intensive market for a lessor. Additionally, a predictor that forecasts the number of reviews a specific listing will get may be helpful in examining elements that affect a property's popularity.

Language: Jupyter Notebook - Size: 6.76 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 1 - Forks: 0

PrusWielki/advml-proj-1

Logistic Regression with different optimizers in Python from scratch

Language: Jupyter Notebook - Size: 23.4 MB - Last synced at: 9 months ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

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.

Language: Java - Size: 13.7 KB - Last synced at: 11 months ago - Pushed at: about 3 years ago - Stars: 19 - Forks: 3

saadlabyad/aslsd

Parametric estimation of multivariate Hawkes processes with general kernels.

Language: Python - Size: 12.5 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 9 - Forks: 1

LiYan-97/CG_SF64

This code uses computational graph and neural network to solve the five-layer traffic demand estimation in Sioux Falls network. It also includes comparison of models and 10 cross-validations.

Language: Python - Size: 8.32 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

NILAY2233/Machine_Learning--Learning-Gradient-Descent-optimization-technique

Gradient Descent is a technique used to fine-tune machine learning algorithms with differentiable loss functions. It's an open-ended mathematical expression, tirelessly calculating the first-order derivative of a loss function and making precise parameter adjustments.

Language: Jupyter Notebook - Size: 230 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

hpca-uji/PyDTNN

PyDTNN - Python Distributed Training of Neural Networks

Language: Python - Size: 12.4 MB - Last synced at: about 1 year ago - Pushed at: over 3 years ago - Stars: 11 - Forks: 4

zillur-av/neural-networks

This is a python implementation of artificial neural network from scratch.

Language: Jupyter Notebook - Size: 86.9 KB - Last synced at: about 1 year ago - Pushed at: about 2 years ago - Stars: 0 - Forks: 0

NILAY2233/Machine_Learning---Learning-Gradient-Descent-optimization-techniques

Language: Jupyter Notebook - Size: 718 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

heydarimo/Intelligent-Systems-Course

This repository contains materials and course projects during attending the Intelligent Systems Course, for more detailed information please have a look at my Final_Report files which have been separately uploaded for each of the projects and consist of all required information about the implementations, analyses, and anything else you may concern about that!

Language: Jupyter Notebook - Size: 23.9 MB - Last synced at: 10 months ago - Pushed at: almost 3 years ago - Stars: 2 - Forks: 0

IsmaelMekene/Metaheuristics--Stochastic-Optimization

Application and illustration of a wide range optimization methods

Language: Jupyter Notebook - Size: 8.6 MB - Last synced at: about 1 year ago - Pushed at: over 4 years ago - Stars: 1 - Forks: 0

sdevalapurkar/linear-regression-models

⚛️ Experimenting with three different algorithms to train linear regression models

Language: Python - Size: 1.98 MB - Last synced at: over 1 year ago - Pushed at: over 6 years ago - Stars: 0 - Forks: 1

pascal-canuel/YAIL

YAIL (Yet another AI Library) ⛵

Language: C++ - Size: 808 MB - Last synced at: over 1 year ago - Pushed at: over 6 years ago - Stars: 0 - Forks: 1

MohammadYasinKarbasian/Machine-Learning-Homeworks

This repository contains my solutions and implementations for assignments assigned during the Machine Learning course.

Language: Jupyter Notebook - Size: 1.65 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

AhmetZamanis/UsedCarKicksClassification

Imbalanced classification with scikit-learn and PyTorch Lightning.

Language: Python - Size: 11.8 MB - Last synced at: about 2 months ago - Pushed at: about 2 years ago - Stars: 1 - Forks: 0

wenyuzhao/Multilayer-Perceptron

Multilayer Perceptron based on NumPy

Language: Python - Size: 14.6 KB - Last synced at: 5 months ago - Pushed at: about 8 years ago - Stars: 2 - Forks: 1

thomaswsu/Stochastic-Gradient-Boosting-and-Adaboost

In this repository we evaluate the performance of Stochastic Boosting and Traditional Boosting methods in two ways. The first is through evaluating the amount of data needed for each method to effectively generalize the classification problem. The second is effect of increasing the complexity of Weak Learner. How does a Weak Learner perform as it becomes for complex? Is it still able to generalize the classification problem in the same number of epochs?

Language: Jupyter Notebook - Size: 72 MB - Last synced at: over 1 year ago - Pushed at: over 4 years ago - Stars: 0 - Forks: 0