balditommaso/PyLandscape

This project propose the loss landscape analysis as effective methodology to understand the robustness against natural perturbation of QNN.

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jhermienpaul/ibm-data-science-program

Practical exercises and resources from the 12-course IBM Data Science Professional Certificate program, tackling DS fundamentals, machine learning, data analysis, and visualization using Python

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giacomolat/RealEstateAI-Solutions---A-forecasting-model-for-the-housing-market

This project applies regularization techniques (Ridge, Lasso, and Elastic Net) to improve real estate price forecasting. This project focuses on reducing overfitting and increasing the stability of regression models' predictions

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najeh-halawani/Improving-Generalization-Deepfake-Detectors

An implementation of a deepfake detection model that uses gradient regularization to improve robustness against adversarial attacks. This approach perturbs the mean and standard deviation of shallow layers in an EfficientNetB0 backbone to enhance generalization and defend against attacks like FGSM and PGD.

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bythyag/nn-regularization-exps

experiment design to study overfitting, underfitting, and different regularization methods.

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Andersoncrs/Prediccion-del-Precio-de-Vehiculos-Un-Enfoque-con-Regresion-Lineal-y-Regularizacion

Este proyecto tiene como objetivo predecir el precio de vehículos usados utilizando técnicas de regresión lineal y regularización Lasso. A través del análisis y procesamiento de datos, se construye un modelo predictivo preciso e interpretable basado en las características más relevantes de cada vehículo.

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dipterix/spfda

Functional Group Bridge for Simultaneous Regression and Support Estimation (https://arxiv.org/abs/2006.10163)

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edserranoc/Inverse_Problems_Course

Inverse Problems Course.

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adeyemiadeoye/SelfConcordantSmoothOptimization.jl

Self-concordant Smoothing for Large-Scale Convex Composite Optimization

Language: Julia - Size: 203 MB - Last synced at: 3 days ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

chandar-lab/PatchUp

Language: Python - Size: 54.7 KB - Last synced at: 3 months ago - Pushed at: over 2 years ago - Stars: 39 - Forks: 7

JoseRuiz01/DeepLearningTraining

Experiments with different deep learning architectures for image classification

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drkocoglu/Machine_Learning_Class

Machine Learning Class - ECE - TTU - Spring 2020

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ArtZaragozaGitHub/NN--P4_Predicting_Customers_Likely_to_Abandon_Bank_Services

Analyze the customer data, build a neural network to help the operations team identify the customers that are more likely to churn, and provide recommendations on how to retain such customers

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Andersoncrs/Regularizacion_Lasso_en_modelos_de_regresion_lineal

Este repositorio contiene un análisis detallado sobre la implementación de la regularización Lasso en modelos de regresión lineal para predecir el precio de vehículos. Se parte de un conjunto de datos limpio y se aplican diversas transformaciones y modelados para mejorar la precisión de las predicciones.

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jonathankoop/remreg

An R package to evaluate regularization in Relational Event Models

Language: R - Size: 201 KB - Last synced at: 6 months ago - Pushed at: 6 months ago - Stars: 0 - Forks: 0

alef-s/ReneWind_Model_Tuning

The objective is to build various classification models, tune them and find the best one that will help identify failures so that the generator could be repaired before failing/breaking and the overall maintenance cost of the generators can be brought down.

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mdpetters/RegularizationTools.jl

A Julia package to perform Tikhonov regularization for small to moderate size problems.

Language: Julia - Size: 1.89 MB - Last synced at: 26 days ago - Pushed at: over 2 years ago - Stars: 12 - Forks: 2

isaqueiros/NewspaperSales-predictions-LinearRegression_and_Regularisation

This notebook is a study on the sales of newspapers of a local stand, with intention to predict the newspaper sales performance based on the different features available. For this, 4 sklearn models are applied: Linear Regression, Lasso Regression, Ridge Regression and Elastic Net Regression.

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emilyksanders/CA_dropout_rates

This is an expansion of dsb318-group4 (see repo: dsb318-group4), in which we collaborated to predict high school graduation rates in CA from other trends (e.g., poverty rate, availability of e-cigarettes). Collaboration between Eli and Emily.

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harmanveer-2546/Guide-to-Regularization

Regularization is a crucial technique in machine learning that helps to prevent overfitting. Overfitting occurs when a model becomes too complex and learns the training data so well that it fails to generalize to new, unseen data.

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codingmoose-dev/StatisticalLearning-WithPythonApplications

Activities include Python basics, linear and logistic regression, cross-validation, tree-based methods, SVMs, deep learning, survival analysis, unsupervised learning, and multiple testing.

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kevjh18/PYRO

Ridge Regression Work

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srosalino/Prediction_of_Seoul_Bikes_Demand

The objective of this project is to predict the number of bicycles needed to be made available each hour in order to make the service as efficient as possible

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saibharath2/linear-regression

linear regression

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claire-yiting-zhang/Stock-Performance-Prediction

Size: 4.88 KB - Last synced at: about 1 year ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 0

juliusgraf/Robust_Image_Segmentation

Conducted advanced research on unsupervised image segmentation using Lipschitz regularity constraints

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alecruces/SafeRoadsCA

Analyzing car accident fatalities to pave the way for preventative measures and safer transportation using Statistical and Machine Learning algorithms

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koushik16/Bias-Variance-Decomposition

Implementing bias-variance-noise decomposition on binary data from Gaussian distributions. Create functions for noise, bias, and variance computation, leveraging Bayes' rule, ridge regression, and model averaging. Aim to visualize error changes with regularization.

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magnusneuman/Modular-GLASSO

Code for the paper "Module-based regularization improves Gaussian graphical models when observing noisy data"

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ez77/Numerical-Methods

Repository with some implementations of algorithms used in Numerical Analysis. From the solution of determined and overdeterminded systems to regularization and non-linear least square problems.

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Tikhon-Radkevich/CourseYandexML

These training sessions in machine learning, conducted by Yandex, are dedicated to classical machine learning. This offers an opportunity to reinforce theoretical knowledge through practice on training tasks.

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shimazadeh/Neural_Networks

the implementation of a multilayer perceptron

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richardd3ng/Image-Reconstruction

Digital Image Reconstruction

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KristofPusztai/MixReg

Stable-Baselines Implementation of MixReg regularization technique for PPO2

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devanshkhare1705/Personalizing-K12-Education

Using deep learning to predict whether students can correctly answer diagnostic questions

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kk289/ML-Regularized_Linear_Regression-Bias_Variance-MATLAB

Here, we implement regularized linear regression to predict the amount of water flowing out of a dam using the change of water level in a reservoir. In the next half, we go through some diagnostics of debugging learning algorithms and examine the effects of bias v.s. variance.

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abs711/Optimizing-representations-for-gait-generation

Using encoder-decoder neural networks to learn representations of personal walking style, and generating person-specific gait for desired activities.

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FaresGh1997/MLDM_HWs

Machine Learning and Data Mining Projects (2022-2023)

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Luehrsen/sd_regularization_images

Pre-Rendered Regularization Images fou use with fine-tuning, especially for the current implementation of "Dreambooth"

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AlePalu/fdaPDE Fork of fdaPDE/fdaPDE

fdaPDE: Physics-Informed Spatial and Functional Data Analysis

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ar8372/CutMix-Regularization-Strategy-to-Train-Strong-Classifiers-with-Localizable-Features

Through this project we will try to understand CutMix by implementing it on a simple problem of cat-vs-dog classification.

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lironmiz/machine-learning-specialization-course

Master fundamental AI concepts and develop practical machine learning skills in the beginner-friendly, 3-course program by AI visionary Andrew Ng

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seunshix/convolutional_neural_networks

Preprocessed the USPS dataset, implemented and compared different network architectures and optimization techniques, applied regularization techniques such as ensembling and dropout, performed adversarial training to evaluate network robustness, and evaluated network performance using metrics such as accuracy, precision, and recall.

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Distraict/Stable-Diffusion-Regularization-Images

A vast assortment of class regularization images in sets of 1500

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mohadeseh-ghafoori/Regularization-Methods

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CarolinaPerdomo/Thesis

Notebooks developed in Mathematica for my Ph.D. thesis and other resources

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mbidu/Machine-Learning-Projects

Machine Learning Projects

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faketano/Determining-critical-components-for-development

Use of Supervised and Unsupervised Learning Techniques to determine critical components for development of countries

Language: R - Size: 28.3 KB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 0

aliyzd95/Optimization-and-Regularization-from-scratch

Implementation of optimization and regularization algorithms in deep neural networks from scratch

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dizam92/pyTorchReg

Applied Sparse regularization (L1), Weight decay regularization (L2), ElasticNet, GroupLasso and GroupSparseLasso to Neuronal Network.

Language: Python - Size: 3.15 MB - Last synced at: over 2 years ago - Pushed at: about 3 years ago - Stars: 36 - Forks: 8

sauriii98/Deep-Learning-algorithms

Implementation of all basic algorithms needed in Deep Learning

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inimah/protoinfomax

Code and Data sets for the EMNLP-2021-Findings Paper "ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection"

Language: Python - Size: 24.4 MB - Last synced at: over 2 years ago - Pushed at: over 3 years ago - Stars: 3 - Forks: 3

Sushmitha-93/CMPE275---Machine-Learning-Assigments-and-Labs

Machine Learning Course Topic wise Assignments and Projects

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victor7246/WNUT-2020-Task-2

System developed by team datamafia in WNUT 2020 Task 2: Identification of informative COVID-19 English Tweets

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GeorgeVern/AFTERV1.0

Python source code for EMNLP 2020 Findings paper: "Domain Adversarial Fine-Tuning as an Effective Regularizer".

Language: Python - Size: 435 KB - Last synced at: over 2 years ago - Pushed at: about 3 years ago - Stars: 8 - Forks: 2

ADITYASHAH-IITP/Measure-of-Diabetes-progression

A quantitative measure of disease progression one year after baseline

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ADITYASHAH-IITP/IRIS_Dataset_Classfication

Iris plants dataset

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steve303/stat542code2-RegularizationMethods

Objectives: 1) Implement coordinate descent algorithm for lasso penalization using R, 2) Document performance differences between lasso and ridge penalization when performing regression on a reference data set versus reference data with added spurious predictors

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KaushikPalani/Stock_Market_Forecasting

This work attempts to generalize a stock forecasting neural network using Bayesian regularization so that predictions can be performed without an overfitted model, considering the highly volatile market these days.

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ashishpatel26/Regularization-Collection-Deeplearning

This is Collection of Regularization Deep learning techniques with code and paper

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EliottZemour/cs-433-project-2-lpmc_dcm Fork of CS-433/cs-433-project-2-lpmc_dcm

Regularized maximum likelihood estimation for discrete choice models on the LPMC dataset

Language: Python - Size: 3.85 MB - Last synced at: about 2 years ago - Pushed at: almost 4 years ago - Stars: 0 - Forks: 0

rpatrik96/lod-wmm-2019

Supplementary code for the paper "Stochastic Weight Matrix-based Regularization Methods for Deep Neural Networks" - an accepted paper of LOD2019

Language: Python - Size: 111 KB - Last synced at: about 1 month ago - Pushed at: about 6 years ago - Stars: 3 - Forks: 0

DataEngel/World-Happiness-Report--Robust-regressions

Constructing a linear regression model which may help us predict the happiness score in the 155 countries.

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cassnutt/Detecting_pneumonia

Using over 5,800 images of chest radiographs, I utilized machine learning and neural networks to predict when pneumonia is present. The best model was able to predict over 80% accuracy on the test data with a false negative rate that was less than 3%.

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Subangkar/Improving-Deep-Neural-Networks-Deeplearning.ai-Coursera-Assignments

Notebooks of programming assignments of Improving Deep Neural Networks course of deeplearning.ai on coursera in August-2019

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KrystynaGrzesiak/gslope

Sparse Gaussian graphical models with Sorted L-One Penalized Estimation

Language: R - Size: 353 KB - Last synced at: 5 months ago - Pushed at: almost 5 years ago - Stars: 3 - Forks: 0

alejandrods/Noise-Regularization-Method-Neural-Network

Adding noise as regularization method to reduce overffiting in neural networks

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