ArshTiwari2004/Sahyog

Centralized Disaster Response and Inventory Management System that leverages AI and Google Cloud Technologies to predict disasters, optimize resource management, and provide real-time coordination.

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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.

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abisliouk/IE675b-machine-learning

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

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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.

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fardinabbasi/Logistic_Regression

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

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ishreya09/Skin-Cancer-Detection

Developed a CNN model to classify skin moles as benign or malignant using a balanced dataset from Kaggle, achieving a test accuracy of 81.82% and an AUC of 89.06%. Implemented data preprocessing by resizing images to 224x224 pixels and normalizing pixel values, enhancing model performance and stability.

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saminheydarian/DeepLearning_Course_2021

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

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UnixJunkie/linwrap

Wrapper on top of liblinear-tools

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bhattbhavesh91/regularization-neural-networks

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

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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.

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noshintas96/Linear_Polynomial_Regression_Scratch_with_L2_Regularization

In this project, we aim to implement linear and polynomial regression of 2nd, 3rd, and 4th order from scratch, and apply L2-regularization to the 4th-order polynomial regression. We will perform these tasks using training data and evaluate the performance using different regularization parameters.

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siddharthp30/ridge-regression-house-price-prediction

Implementing Ridge Regression (L2) to predict House Prices

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siddharthiyervarma/CNN-MNIST

The aim was to develop a robust Convolutional Neural Network (CNN) for accurately classifying handwritten digits from the MNIST dataset

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siddharthiyervarma/-DeepSonar_Classifier-

The primary objective of this project is to design and train a deep neural network that can generalize well to new, unseen data, effectively distinguishing between rocks and metal cylinders based on the sonar chirp returns.

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mohadeseh-ghafoori/Coursera-Deep-Learning-Specialization

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the-lans/NeuroRepository

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

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SapanaChaudhary/PyTorch-IL-functions

PyTorch implementation of important functions for WAIL and GMMIL

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dedupeio/rlr 📦

Regularized Logistic Regression

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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.

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marcolagos/nba-dollar-win

How much is the NBA dollar worth in terms of team success?

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SakshithReddyChintala/Multiclass_logistics_classification_pipeline

Multiclass Logistic, Classification Pipeline, Cross Validation, Gradient Descent, Regularization

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zhangyongheng78/Mathematical-Machine-Learning-Algorithm-Implementations

Mathematical machine learning algorithm implementations

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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.

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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.

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BenitaDiop/AnalysisOfCrimeInIndia

The dataset that I am performing this regression analysis on, comes from Kaggle, titled crimes In India. This dataset holds complete information about various aspects of crimes that have taken place in India in a 17 year span, from 2001 to 2018.

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Rahil-07/Neural_Net_from_scratch

Creating Neural Net from scratch using python , Numpy.

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yarrap/Heart-Disease-Prediction

Predicting whether a person has presence of heart disease using Logistic Regression

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tayebiarasteh/DeepLearning_from_scratch

A Deep Learning framework for CNNs and LSTMs from scratch, using NumPy.

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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.

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mohadeseh-ghafoori/Regularization-Methods

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jajokine/Genomics-and-High-Dimensional-Data

MITx - MicroMasters Program on Statistics and Data Science - Data Analysis: Statistical Modeling and Computation in Applications - Second Project

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Praveen2812git/linear_regression_understanding_and_feature_engineering

linear regression with different types and datasets. Understanding of linear regression with Boston dataset using numpy.

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jaiminjariwala/Linear_Regression_Project_Using_ElasticNet_N_GridSearchCV_-House_Price_Prediction-

Linear Regression Project from Udemy course 2022 Python for Machine Learning and Data Science Masterclass

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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

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aliyzd95/Optimization-and-Regularization-from-scratch

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

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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

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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

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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

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

Short description for quick search

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darshil2848/House-Price-Prediction

House Price Analysis and Sales Price Prediction

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MoinDalvs/Different_types_Linear_Regressions

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arianhaddadi/Recommender-System

a Recommender System Using Collaborative-Filtering Technique.

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pwc2/ridge-regression

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

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kinoute/Elyane

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

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kashishpandey/CS510-VideoGameAnalysis

Chapman University CS-510 Computing For Scientists Final Project

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gabrielegilardi/FeedForwardNN

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

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gabrielegilardi/ClassificationNN

Multivariate Classification Using a Feed-Forward Neural Network and Backpropagation.

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gabrielegilardi/RegressionGDO

Multivariate Linear and Logistic Regression Using Gradient Descent Optimization.

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mrshub/Wat_Lip_Removal_L2

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

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SwikarGautam/DeepDenseNetwork

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

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akshaykhadse/ml-linear-regression

Repository for Assignment 1 for CS 725

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nishanthbhat07/MachineLearning_Python

This repository is about machine learning algorithms

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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.

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cryptomanic/DeepLearningBasics

Just exploring Deep Learning

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Abdulrahman-Adel/Black-Friday-Sales-Prediction

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jamesneve/go-neural-network

Modifiable neural network

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rohanpillai20/SGD-for-Linear-Regression-with-L2-Regularization

Code for Stochastic Gradient Descent for Linear Regression with L2 Regularization

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Pirols/weather_images_classifier-mlhw2

This repository contains the second, of 2, homework of the Machine Learning course taught by Prof. Luca Iocchi.

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sinturkgozde/Keras-CNN-Fashion-MNIST

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

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SwikarGautam/ConvoNet

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

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cnavneet/notMNIST

Identifying text in images in different fonts using deep neural network techniques.

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adityachechani/Neural-Networks

1. Understand how neural networks work 2. Implement a simple neural network 3. Understand the role of different parameters of a neural network, such as learning rate

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lingxuez/RLR

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

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