ZerojumpLine/OverfittingUnderClassImbalance

[MICCAI2019 & TMI2020] Overfitting under Class Imbalance: Anaylsis and Improvements for Medical Image Segmentation.

Language: Python - Size: 42 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 20 - Forks: 2

tazriahelal/Dropout_Regularization-

Dropout in Deep Learning

Language: Jupyter Notebook - Size: 60.5 KB - Last synced at: about 2 years ago - Pushed at: about 2 years ago - Stars: 8 - Forks: 0

Juhi-Purswani/Offline_Signature_Verification

Classification of signatures in image format as genuine or fake. Created two models - one from scratch using deep learning layers and other using pre trained model VGG16. Before training used image pre processing techniques as well.

Language: Jupyter Notebook - Size: 266 KB - Last synced at: about 2 years ago - Pushed at: almost 6 years ago - Stars: 2 - Forks: 2

HarikrishnanK9/Health_Profile_Analysis

Health Profile Analysis:Revealing Disorder Paterns,Medication Guidance and Risk Classification-ML Project

Language: Jupyter Notebook - Size: 3.42 MB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

surafiel-habib/Transformer-Based-Amharic-to-English-Machine-Translation-with-Character-Embedding-and-Combined-Regul

Language: Jupyter Notebook - Size: 9.04 MB - Last synced at: 4 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

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.

Language: Jupyter Notebook - Size: 879 KB - Last synced at: 28 days ago - Pushed at: 8 months ago - Stars: 0 - Forks: 0

mode1990/Bayesian-PRS

Bayesian PRS methods model uncertainty in effect size estimates and shrink small effect sizes to mitigate spurious associations and biases from sample overlap. By using full posterior distributions rather than point estimates, they effectively account for estimation errors and reduce the impact of artificially inflated associations.

Language: Jupyter Notebook - Size: 158 KB - Last synced at: 9 months ago - Pushed at: 9 months ago - Stars: 0 - Forks: 0

AmbreenMahhoor/What-is-Elastic-Net-Regression

Language: Jupyter Notebook - Size: 2.93 KB - Last synced at: 9 months ago - Pushed at: 9 months ago - Stars: 0 - Forks: 0

NiharJani2002/Kaggle-Intro-To-Machine-Learning

Intro to Machine Learning Course By Kaggle

Language: Jupyter Notebook - Size: 86.9 KB - Last synced at: 10 months ago - Pushed at: 10 months ago - Stars: 0 - Forks: 0

Ashwani-Verma-07/Predicting-Performance-of-Advertisement

A Performance Study of Naive Bayes Classifier in Advertisement Analysis

Language: Jupyter Notebook - Size: 984 KB - Last synced at: 10 months ago - Pushed at: 10 months ago - Stars: 0 - Forks: 0

SuyashMali/data-augmentation-cnn

This repository explores how data augmentation helps mitigate overfitting in CNNs with limited training data.

Language: Jupyter Notebook - Size: 298 MB - Last synced at: 10 months ago - Pushed at: 10 months ago - Stars: 0 - Forks: 0

Udacity-MachineLearning-Internship/ReducingOverfitting

Reducing overfitting in perdiction in decision trees

Language: Jupyter Notebook - Size: 20.5 KB - Last synced at: about 2 months ago - Pushed at: 12 months ago - Stars: 0 - Forks: 0

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.

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

Neyung/DV

Data Analysis and Visualization in the US Health Insurance industry - UEH

Language: Jupyter Notebook - Size: 17.3 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

Jimoh1993/UM6P-SCI-Data-Science-California-Housing-EDA-Project

This is the dataset used in the second chapter of Aurélien Géron's recent book 'Hands-On Machine learning with Scikit-Learn and TensorFlow'. It serves as an excellent introduction to implementing machine learning algorithms because it requires rudimentary data cleaning, has an easily understandable list of variables and sits at an optimal size between being to toyish and too cumbersome.

Language: Jupyter Notebook - Size: 578 KB - Last synced at: 8 months ago - Pushed at: over 3 years ago - Stars: 0 - Forks: 0

akshayratnawat/BoostingAlgorithms

This project explores the working of various Boosting algorithms and analyzes the results across different algorithms. Algorithms Used are: Random Forest, Ada Boost, Gradient Boost and XG Boost

Language: HTML - Size: 1.33 MB - Last synced at: about 2 years ago - Pushed at: about 4 years ago - Stars: 0 - Forks: 0