l2-regularization

SSQ/Coursera-Ng-Improving-Deep-Neural-Networks-Hyperparameter-tuning-Regularization-and-Optimization

Short description for quick search

the-lans/NeuroRepository

Фреймворк для построения нейронных сетей, комитетов, создания агентов с параллельными вычислениями.

dedupeio/rlr

Regularized Logistic Regression

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.

alejandrods/Analysis-of-the-robustness-of-NMF-algorithms

Analysis of the robustness of non-negative matrix factorization (NMF) techniques: L2-norm, L1-norm, and L2,1-norm

jamesneve/go-neural-network

Modifiable neural network

UnixJunkie/linwrap

Wrapper on top of liblinear-tools

DunittMonagas/Improving-Deep-Neural-Networks-Hyperparameter-tuning-Regularization-and-Optimization

Curso Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization. Segundo curso del programa especializado Deep Learning. Este repositorio contiene todos los ejercicios resueltos. https://www.coursera.org/learn/neural-networks-deep-learning

kinoute/Elyane

An OOP Deep Neural Network using a similar syntax as Keras with many hyper-parameters, optimizers and activation functions available.

mrshub/Wat_Lip_Removal_L2

Water and lipid signal removal in MRSI by L2 regularization (submitted by Liangjie Lin)

SapanaChaudhary/PyTorch-IL-functions

PyTorch implementation of important functions for WAIL and GMMIL

bhattbhavesh91/regularization-neural-networks

Simple Demo to show how L2 Regularization avoids overfitting in Deep Learning/Neural Networks

akshaykhadse/ml-linear-regression

Repository for Assignment 1 for CS 725

aliyzd95/Optimization-and-Regularization-from-scratch

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

bohaterewicz/Sales_forecasting_model

The aim was to create and implement a predictive model that can forecast the number of items sold for a period of 8 weeks ahead.

fardinabbasi/Logistic_Regression

Implementing logistic regression with L2 regularization from scratch to classify circular datasets by mapping the feature space into higher dimensions.

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.

gabrielegilardi/FeedForwardNN

Multivariate Regression and Classification Using a Feed-Forward Neural Network and Gradient Descent Optimization.

GhazaleZe/Investigate_Classifiers

The point is to investigate three types of classifiers (linear classifier with feature selection, linear classifier without feature selection, and a non-linear classifier) in a setting where precision and interpretability may matter.

KanishkNavale/Text-Mining-with-TF-IDF-and-Cosine-Similarity

A simple python repository for developing perceptron based text mining involving dataset linguistics preprocessing for text classification and extracting similar text for a given query.

lingxuez/RLR

My java implementation of scalable on-line stochastic gradient descent for regularized logistic regression

mmaric27/BasicDNN

Generic L-layer 'straight in Python' fully connected Neural Network implementation using numpy.

mohadeseh-ghafoori/Coursera-Deep-Learning-Specialization

MoinDalvs/Different_types_Linear_Regressions

pwc2/ridge-regression

Implementation of linear regression with L2 regularization (ridge regression) using numpy.

saminheydarian/DeepLearning_Course_2021

Deep Learning Course | Home Works | Spring 2021 | Dr. MohammadReza Mohammadi

shouryasimha/Ships-In-Satellite-images

Satellite imagery provides unique insights into various markets, including agriculture, defense and intelligence, energy, and finance. New commercial imagery providers, such as Planet, are using constellations of small satellites to capture images of the entire Earth every day. This flood of new imagery is outgrowing the ability for organizations to manually look at each image that gets captured, and there is a need for machine learning and computer vision algorithms to help automate the analysis process. The aim is to help address the difficult task of detecting the location of large ships in satellite images. Automating this process can be applied to many issues including monitoring port activity levels and supply chain analysis.

sinturkgozde/Keras-CNN-Fashion-MNIST

Image Classification with CNN using Tensorflow backend Keras on Fashion MNIST dataset

SwikarGautam/ConvoNet

A framework for implementing convolutional neural networks and fully connected neural network.

SwikarGautam/DeepDenseNetwork

Fully connected neural network with Adam optimizer, L2 regularization, Batch normalization, and Dropout using only numpy

zhangyongheng78/Mathematical-Machine-Learning-Algorithm-Implementations

Mathematical machine learning algorithm implementations

abisliouk/IE675b-machine-learning

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

MohammedSaqibMS/Regularization

This repository implements a 3-layer neural network with L2 and Dropout regularization using Python and NumPy. It focuses on reducing overfitting and improving generalization. The project includes forward/backward propagation, cost functions, and decision boundary visualization. Inspired by the Deep Learning Specialization from deeplearning.ai.

barisgudul/Ridge_vs_Lasso_Analysis

This project compares the effects of Ridge (L2) and Lasso (L1) regression models on clinical data.

DavidAlmagro/WineQuality_Regularization

Predicting wine quality scores using regression models with L1 and L2 regularisation techniques