l1-regularization

There are 0 repository under l1-regularization topic.

foolwood / pytorch-slimming
Learning Efficient Convolutional Networks through Network Slimming, In ICCV 2017.
weight-pruning pytorch l1-regularization fast-inference deep-learning
Language:Python 576
carnotresearch / cr-sparse
Functional models and algorithms for sparse signal processing
sparse-representations jax wavelets convex-optimization linear-operators compressive-sensing functional-programming l1-regularization sparse-linear-systems lasso sparse-bayesian-learning basis-pursuit
Language:Jupyter Notebook 95
rfeinman / pytorch-lasso
L1-regularized least squares with PyTorch
dictionary-learning l1-regularization lasso least-squares pytorch sparse-coding
Language:Python 70
sandipanpaul21 / Logistic-regression-in-python
Logistic Regression technique in machine learning both theory and code in Python. Includes topics from Assumptions, Multi Class Classifications, Regularization (l1 and l2), Weight of Evidence and Information Value
logistic-regression regularization l1-regularization l2-regularization lasso-regression ridge-regression elastic-net-regression multiclass-logistic-regression weight-of-evidence information-value logistic-regression-assumptions
Language:Jupyter Notebook 18
JasonSloan / yolov8-prune
Yolov8-pruning based on constraint of BN layer gamma values.
l1-regularization model-pruning model-sparsity yolov8-pruning
Language:Python 13
Image-Reconstructor-FISTA-proximal-method-on-wavelets-transform
EliaFantini / Image-Reconstructor-FISTA-proximal-method-on-wavelets-transform
An Image Reconstructor that applies fast proximal gradient method (FISTA) to the wavelet transform of an image using L1 and Total Variation (TV) regularizations
fista ista l1-regularization machine-learning proximal-gradient-descent proximal-policy-optimization total-variational-denoising data-science denoising-images image-reconstruction optimization-methods python pytorch total-variation matplotlib skimage
Language:Python 11
mansipatel2508 / Network-Intrusion-Detection-with-Feature-Extraction-ML
The given information of network connection, model predicts if connection has some intrusion or not. Binary classification for good and bad type of the connection further converting to multi-class classification and most prominent is feature importance analysis.
network-intrusion-detection feature-extraction feature-selection feature-importance binary-classification multiclass-classification confusion-matrix roc-curve pearson-correlation-coefficient rfe l1-regularization l2-regularization fclassif select-percentile extrat-tree-classifier rfc
Language:Jupyter Notebook 11
malena1906 / Pruning-Weights-with-Biobjective-Optimization-Keras
Overparameterization and overfitting are common concerns when designing and training deep neural networks. Network pruning is an effective strategy used to reduce or limit the network complexity, but often suffers from time and computational intensive procedures to identify the most important connections and best performing hyperparameters. We suggest a pruning strategy which is completely integrated in the training process and which requires only marginal extra computational cost. The method relies on unstructured weight pruning which is re-interpreted in a multiobjective learning approach. A batchwise Pruning strategy is selected to be compared using different optimization methods, of which one is a multiobjective optimization algorithm. As it takes over the choice of the weighting of the objective functions, it has a great advantage in terms of reducing the time consuming hyperparameter search each neural network training suffers from. Without any a priori training, post training, or parameter fine tuning we achieve highly reductions of the dense layers of two commonly used convolution neural networks (CNNs) resulting in only a marginal loss of performance. Our results empirically demonstrate that dense layers are overparameterized as with reducing up to 98 % of its edges they provide almost the same results. We contradict the theory that retraining after pruning neural networks is of great importance and opens new insights into the usage of multiobjective optimization techniques in machine learning algorithms in a Keras framework. The Stochastic Multi Gradient Descent Algorithm implementation in Python3 is for usage with Keras and adopted from paper of S. Liu and L. N. Vicente: "The stochastic multi-gradient algorithm for multi-objective optimization and its application to supervised machine learning". It is combined with weight pruning strategies to reduce network complexity and inference time.
unstructured-weight-pruning multiobjective-learning soft-pruning sparsity automl network-compression stochastic-multi-gradient l1-regularization dichotomic-serach dense-layers keras tensorflow image-classification machine-learning ai python3
Language:Python 9
jaydu1 / SparsePortfolio
High Dimensional Portfolio Selection with Cardinality Constraints
portfolio-optimization high-dimensional-statistics cardinality-constraints l1-regularization
Language:Python 6
Arijit-datascience / CNN_BatchNormalization_Regularization
MNIST Digit Prediction using Batch Normalization, Group Normalization, Layer Normalization and L1-L2 Regularizations
batchnormalization groupnormalization layernorm groupnorm l1-regularization cnn pytorch regularization dropout
Language:Jupyter Notebook 3
DolbyUUU / regression_algorithm_implementation_python
Regression algorithm implementaion from scratch with python (OLS, LASSO, Ridge, robust regression)
python regression regularization l1-regularization l2regularization lasso-regression maximum-likelihood-estimation ordinary-least-squares quantile-regression robust-regresssion
Language:Python 2
ivannz / l1_tf
A wrapper for L1 trend filtering via primal-dual algorithm by Kwangmoo Koh, Seung-Jean Kim, and Stephen Boyd
primal-dual-algorithm l1-regularization trend filter
Language:C 2
aliyzd95 / Optimization-and-Regularization-from-scratch
Implementation of optimization and regularization algorithms in deep neural networks from scratch
deep-learning deep-neural-networks optimization optimization-algorithms regularization regularization-methods adagrad adam gradient-descent rmsprop l1-regularization l2-regularization cifar cifar-10 cifar10
Language:Python 1
Arek-KesizAbnousi / LASSO-vs-SCAD-vs-MCP_Estimators
Comparing Three Penalized Least Squares Estimators: LASSO,SCAD and MCP.
bias-variance l1-regularization lasso-regression
Language:R 1
federicoarenasl / Regularization-techniques-on-NNs
During this study we will explore the different regularisation methods that can be used to address the problem of overfitting in a given Neural Network architecture, using the balanced EMNIST dataset.
neural-networks l2-regularization l1-regularization dropout machine-learning deep-learning data-science
Language:Jupyter Notebook 1
giamuhammad / neuralnet_optimization_based_on_ga
Forecasting for AirQuality UCI dataset with Conjugate Gradient Artificial Neural Network based on Feature Selection L1 Regularized and Genetic Algorithm for Parameter Optimization
forecasting predictive-modeling artificial-neural-networks genetic-algorithm optimization conjugate-gradient-descent regression-models l1-regularization
Language:Jupyter Notebook 1
MoinDalvs / Different_types_Linear_Regressions
data-science linear l1-regularization l2-regularization lasso-regression linear-regression polynomial-regression ridge-regression
Language:Jupyter Notebook 1
zhangyongheng78 / Mathematical-Machine-Learning-Algorithm-Implementations
Mathematical machine learning algorithm implementations
kernel-prediction l1-regularization l2-regularization lda svm generative-model neural-networks machine-learning
Language:Python 1
ayan-chattaraj / house_price_prediction
I executed this assignment for a US-based housing company named Surprise Housing, wherein a regression model with regularisation was used to predict the actual value of the prospective properties and decide whether to invest in them or not
eda l1-regularization l2-regularization lasso-regression linear-regression ridge-regression
Language:Jupyter Notebook 0
darshil2848 / House-Price-Prediction
House Price Analysis and Sales Price Prediction
eda exploratory-data-analysis l1-regularization l2-regularization lasso-regression python rfe ridge-regression sklearn statsmodels
Language:Jupyter Notebook 0
Manjurkreddy / EndtoendML
Using this ML file can anyone develop any machine learning alogitham in regression. almost 99.99 percent all regression models has been used. We need to pick the best machine learning algoritham using this file. Cheers Manju.
correlation-analysis feature-engineering l1-regularization machine-learning-algorithms normaldistribution predictive-modeling regression regression-algorithms
Language:Jupyter Notebook 0
pjkresearch / Sorted-L1-Norm
Minimum working example for using the Sorted L1 Norm in a regression and mean-variance framework. The codes are free to use for research purposes only with the propper citation. Commercial use is strictly forbidden and the rights remain with the authors. For citing purposes please refer to the JBF version: https://www.sciencedirect.com/science/article/abs/pii/S0378426619302614
l1-regularization portfolio regularization
Language:MATLAB 0
saisriramyerubandi / LogisticRegression
Classification Using Logistic Regression by Making a Neural Network Model. This project also includes comparison of Model performance when different regularization techniques are used
logistic-regression neural-networks regularization-methods regularization-techniques l1-regularization l2-regularization classification-algorithm
Language:Jupyter Notebook 0
SakshithReddyChintala / Multiclass_logistics_classification_pipeline
Multiclass Logistic, Classification Pipeline, Cross Validation, Gradient Descent, Regularization
cross-validation l1-regularization l2-regularization multiclass-logistic-regression
Language:Jupyter Notebook 0
Tim907 / oblivious_sketching_varreglogreg
This is the accompanying code repository for the ICLR 2023 publication "Almost Linear Constant-Factor Sketching for 𝓁₁ and Logistic Regression" by Alexander Munteanu, Simon Omlor and David P. Woodruff.
iclr iclr2023 l1-regularization logistic-regression python sampling sketching
Language:Jupyter Notebook 0
barisgudul / Ridge_vs_Lasso_Analysis
This project compares the effects of Ridge (L2) and Lasso (L1) regression models on clinical data.
diabetes-dataset feature-importance feature-importance-analysis hyperparameter-tuning l1-regularization l2-regularization lasso-regression parameter-optimization regression-analysis regularization-techniques ridge-regression
Language:Jupyter Notebook
DavidAlmagro / WineQuality_Regularization
Predicting wine quality scores using regression models with L1 and L2 regularisation techniques
l1-regularization l2-regularization machine-learning regression-models regularization supervised-learning wine-dataset
Language:Jupyter Notebook
devosmitachatterjee2018 / Statistical_Learning_for_Big_Data_Report12062020
The project encompasses the statistical analysis of a high-dimensional data using different classification, feature selection, clustering and dimension reduction techniques.
elastic-net-regression kmeans-clustering l1-regularization logistic-regression normalization standardization tsne
Language:Python
FabrizioMusacchio / L1_and_L2_regularization
This repository contains the code for the blog post on Understanding L1 and L2 regularization in machine learning. For further details, please refer to this post.
cnn hyperparameter-optimization hyperparameter-tuning l1-regularization l2-regularization loss-functions penalization penalty python regularization
Language:Python
hwixley / EMNIST-NeuralNet-Regularisation-Experiments
A study of the problem of overfitting in deep neural networks, how it can be detected, and prevented using the EMNIST dataset. This was done by performing experiments with depth and width, dropout, L1 & L2 regularization, and Maxout networks.
dropout emnist l1-regularization l2-regularization maxout-networks overfitting
Language:Jupyter Notebook
hwixley / MLP-coursework1-report
Machine Learning Practical - Coursework 1 Report: a study of the problem of overfitting in deep neural networks, how it can be detected, and prevented using the EMNIST dataset. This was done by performing experiments with depth and width, dropout, L1 & L2 regularization, and Maxout networks.
dropout emnist l1-regularization l2-regularization maxout-networks overfitting
Language:TeX
jianninapinto / COVID-19-Detection-using-Multilayer-Perceptron-Neural-Network
Used a Multilayer Perceptron (MLP) neural network to detect COVID-19 in lung scans.
adam-optimizer binary-crossentropy keras relu-activation sigmoid-activation tensorflow callbacks dropout early-stopping l1-regularization l2-regularization machine-learning neural-network tensorboard-visualization
Language:Jupyter Notebook
saritha28 / Logistic-Regression
Implementation of Logistic Regression with L1 Regularization from scratch
l1-regularization logistic-regression regression
Language:Jupyter Notebook
tuhinaprasad28 / Multiclass-Logistics-and-Classification-Pipelines
Multiclass Logistic, Classification Pipeline, Cross Validation, Gradient Descent, Regularization
classification-pipeline cross-validation gradientdescent l1-regularization l2-regularization multiclass-logistic-regression regularization-parameters
Language:Jupyter Notebook
unnatibshah / Time-Series-Classification-Part2
Time Series Classification Part 2 Binary and Multiclass Classification. An interesting task in machine learning is classification of time series. In this problem, we will classify the activities of humans based on time series obtained by a Wireless Sensor Network.
binary-classification-using-logistic-regression bootstrap confidence-intervals confusion-matrix cross-validation gaussian l1-regularization multi-class-classification naive-bayes-classifier p-value roc-auc-curve standard-deviation
Language:Jupyter Notebook
yixiao-zeng / DKLasso
This repository is for the development version of DKLasso and DKLasso+.
deep-learning gaussian-processes kernel-methods l1-regularization
Language:Jupyter Notebook

l1-regularization

foolwood / pytorch-slimming

carnotresearch / cr-sparse

rfeinman / pytorch-lasso

sandipanpaul21 / Logistic-regression-in-python

JasonSloan / yolov8-prune

EliaFantini / Image-Reconstructor-FISTA-proximal-method-on-wavelets-transform

mansipatel2508 / Network-Intrusion-Detection-with-Feature-Extraction-ML

malena1906 / Pruning-Weights-with-Biobjective-Optimization-Keras

jaydu1 / SparsePortfolio

Arijit-datascience / CNN_BatchNormalization_Regularization

DolbyUUU / regression_algorithm_implementation_python

ivannz / l1_tf

aliyzd95 / Optimization-and-Regularization-from-scratch

Arek-KesizAbnousi / LASSO-vs-SCAD-vs-MCP_Estimators

federicoarenasl / Regularization-techniques-on-NNs

giamuhammad / neuralnet_optimization_based_on_ga

MoinDalvs / Different_types_Linear_Regressions

zhangyongheng78 / Mathematical-Machine-Learning-Algorithm-Implementations

ayan-chattaraj / house_price_prediction

darshil2848 / House-Price-Prediction

Manjurkreddy / EndtoendML

pjkresearch / Sorted-L1-Norm

saisriramyerubandi / LogisticRegression

SakshithReddyChintala / Multiclass_logistics_classification_pipeline

Tim907 / oblivious_sketching_varreglogreg

barisgudul / Ridge_vs_Lasso_Analysis

DavidAlmagro / WineQuality_Regularization

devosmitachatterjee2018 / Statistical_Learning_for_Big_Data_Report12062020

FabrizioMusacchio / L1_and_L2_regularization

hwixley / EMNIST-NeuralNet-Regularisation-Experiments

hwixley / MLP-coursework1-report

jianninapinto / COVID-19-Detection-using-Multilayer-Perceptron-Neural-Network

saritha28 / Logistic-Regression

tuhinaprasad28 / Multiclass-Logistics-and-Classification-Pipelines

unnatibshah / Time-Series-Classification-Part2

yixiao-zeng / DKLasso