BaraSedih11/ReducingOverfitting

Reducing overfitting in perdiction in decision trees

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zxuuuustupid/cifar10

use CNN to fit cifar10,but overfitting

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bhattbhavesh91/dropout-walkthrough

A repository to show how Dropout in Keras can Prevent Overfitting

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Ashutosh27ind/crossValidationwithLinearRegression

This notebook demonstrates how to do cross-validation (CV) with linear regression as an example (it is heavily used in almost all modelling techniques such as decision trees, SVM etc.). We will mainly use sklearn to do cross-validation.

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LayneH/self-adaptive-training

[TPAMI2022 & NeurIPS2020] Official implementation of Self-Adaptive Training

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modestyachts/ImageNetV2

A new test set for ImageNet

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HannaMeyer/CAST

Developer Version of the R package CAST: Caret Applications for Spatio-Temporal models

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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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HIIT/human-overfitting-in-IML

User Modelling for Avoiding Overfitting in Interactive Knowledge Elicitation for Prediction

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Timie/fourier_head

Simple attempt to create neural network head that would prevent overfitting

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Abdoelabassi/Machine-Learning-Specialization

Machine Learning: Regression and Classification. Andrew presents a course in introduction to machine learning, with practice in regression and classification for the first and second course, and the third course focuses on recommender systems and reinforcement learning.

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BuddyBent/Titanic

We explore the Titanic Kaggle competition, employing advanced analytics and machine learning to accurately predict survival rates. This repository serves as a detailed guide for all levels, offering visual insights, data preprocessing methods, and model comparisons.

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VITA-Group/Alleviate-Robust-Overfitting

[ICLR 2021] "Robust Overfitting may be mitigated by properly learned smoothening" by Tianlong Chen*, Zhenyu Zhang*, Sijia Liu, Shiyu Chang, Zhangyang Wang

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marcojira/fld

PyTorch code for FLD (Feature Likelihood Divergence), FID, KID, Precision, Recall, etc. using DINOv2, InceptionV3, CLIP, etc.

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lijqhs/deeplearning-notes

Notes for Deep Learning Specialization Courses led by Andrew Ng.

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WalidGharianiEAGLE/spatial-kfold

spatial resampling for more robust cross validation in spatial studies

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778569/Model-overfitting-and-Underfitting

Overfitting is often caused by using a model with too many parameters or if the model is too powerful for the given dataset. On the other hand, underfitting is often caused by the model with too few parameters or by using a model that is not powerful enough for the given dataset. In this we are discussing about that.

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emmachollet/ComparSDMsQuantifOverfitSuppInterpr_DataPackage

Package with data, scripts and plots for manuscript "A comparison of machine learning and statistical species distribution models: when overfitting hurts interpretation" (submitted to Ecological Modelling, Dec 2022)

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EnnengYang/Awesome-Forgetting-in-Deep-Learning

A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning. arXiv:2307.09218.

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mathieubolteau/Overfitting_in_ML

Notebook to present overfitting using diabetes dataset.

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sciencenerd880/regression_polynomial

Supervised Learning - Regression Algorithm

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sbrhss/ML-MATLAB

Machine Learning with MATLAB

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11785-Group-9/BMN-Boundary-Matching-Network Fork of JJBOY/BMN-Boundary-Matching-Network

Pytorch implementation of the paper: "BMN: Boundary-Matching Network for Temporal Action Proposal Generation", along with three new modules to address overfitting issues found in the baseline model, and their ablation studies.

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georgezoto/TensorFlow-in-Practice

TensorFlow in Practice Specialization. Join our Deep Learning Adventures community 🎉 and become an expert in Deep Learning, TensorFlow, Computer Vision, Convolutional Neural Networks, Kaggle Challenges, Data Augmentation and Dropouts Transfer Learning, Multiclass Classifications and Overfitting and Natural Language Processing NLP as well as Time Series Forecasting 😀 All while having fun learning and participating in our Deep Learning Trivia games 🎉 http://bit.ly/deep-learning-tf

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georgezoto/Deep-Learning-Adventures

Deep Learning Adventures. Join our Deep Learning Adventures community 🎉 and become an expert in Deep Learning, TensorFlow, Computer Vision, Convolutional Neural Networks, Kaggle Challenges, Data Augmentation and Dropouts Transfer Learning, Multiclass Classifications and Overfitting and Natural Language Processing NLP as well as Time Series Forecasting 😀 All while having fun learning and participating in our Deep Learning Trivia games 🎉 http://bit.ly/deep-learning-tf

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ksonod/deep_learning_with_python

My notebooks on the book "Deep Learning with Python" by Francois Chollet (2018)

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Niranjankumar-c/OverFitting

Analyse OverFitting of data

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sharmaroshan/Don-t-Overfit

It is from Kaggle Competitions where the training dataset is very small and the testing dataset is very large and we have to avoid or reduce overfiting by looking for best possible ways to overcome the most popular problem faced in field of predictive analytics.

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Amitha353/Machine-Learning-Regression

Machine-Learning-Regression

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jamesdhope/regression-models

A series of documented Jupyter notebooks implementing polynomial regression models and model performance analysis

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chiapeilin/Linear-Regression

Write code of linear regression by gradient descent with regularization, and examine the overfitting and underfitting effect.

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klemenjak/nilm-transferability-metrics

Metrics to assess the generalisation ability of NILM algorithms

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sk-g/MNIST_Beginners

Model search in traditional machine learning algorithms (non DL) and DL starter codes on MNIST dataset. This is a good starter code for beginners trying to learn about curse of dimensionality, overfitting and other concepts in general

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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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faezeamin/ML-Logistic-Regression

Logistic Regression algorithm to classify two classes of 2-D data samples using 1) 2-D feature vector & 2) its mapping to a higher dimensional feature space.

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wind-m05/System-Identification-course

Introduction of system identification on an unkown plant with various models.

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szilard/GBM-tune

Tuning GBMs (hyperparameter tuning) and impact on out-of-sample predictions

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felipenufisnor/ML_validation_optimization

Machine Learning: model optimization through hyperparameters

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john-fante/svm-xgboost-lightgbm-ensemble-method-w-noised-dataset

I tried to deal with the overfitting problem using only the AI-generated training set.

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evgenii-nikishin/rl_with_resets

JAX implementation of deep RL agents with resets from the paper "The Primacy Bias in Deep Reinforcement Learning"

Language: Python - Size: 1.1 MB - Last synced: 9 months ago - Pushed: about 2 years ago - Stars: 85 - Forks: 6

fardinabbasi/Decision_Tree

Machine Learning Course [ECE 501] - Spring 2023 - University of Tehran - Dr. A. Dehaqani, Dr. Tavassolipour

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alexmaf90/Machine-Learning-with-Python-

Series of tutorials from Resolving Python on how to compute different algorithms for predicting the future based on tabular data.

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neural-tangjie/NTJ-AIMed_2_Prognosis

:octocat: This repository contains the notes, codes, assignments, quizzes and other additional materials about the course "AI for Medical Prognosis" from DeepLearning.AI Coursera.

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riccmon/Deep-Learning

Lab Sessions of the Deep Learning course - Master's degree in Artificial Intelligence at UniBo

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XinshaoAmosWang/XinshaoAmosWang.github.io

Xinshao Wang, Ex-Postdoc and Ex-Visit Scholar@University of Oxford, Ex-Senior Researcher@ZenithAI

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DanielWicz/ClippedNoiseSoftmax

Clipped Noise Softmax to overcome over-fitting with Softmax - PyTorch implementation

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andressagomes26/CNN_TensorFlow

Curso Convolutional Neural Networks in TensorFlow disponibilizado pela DeepLearning.AI e ministrado através do Coursera

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anshul1004/DecisionTree

Decision Tree classifier from scratch without any machine learning libraries

Language: Python - Size: 24.4 KB - Last synced: 5 months ago - Pushed: over 4 years ago - Stars: 9 - Forks: 6

ChenLiu-1996/CorruptedDataLoader

Pytorch DataLoader wrapper to intentionally mess up, corrupt, shuffle, randomize the input/label correspondence.

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mainkoon81/Study-09-MachineLearning-B

**Supervised-Learning** (with some Kaggle winning solutions and their reason of Model Selection for the given dataset).

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CasperKristiansson/Elements-of-AI-Building-Ai

All exercises for the course Elements of AI - Building AI

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jaunel/Live-detection-of-Sunglasses

Developed CNN model with 93% validation accuracy using techniques like Dropouts & Batch Normalization. Using haar cascade of Computer Vision It is then used to detect sunglasses on real time basis

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stoneMo/SLAVC

Official Codebase of "A Closer Look at Weakly-Supervised Audio-Visual Source Localization" (NeurIPS 2022)

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fau-masters-collected-works-cgarbin/dropout-vs-batch-normalization

Dropout vs. batch normalization: effect on accuracy, training and inference times - code for the paper

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HasibAlMuzdadid/Pet-Image-Classification

Classification between pet images using different approaches on famous cats vs dogs dataset available on kaggle.

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austincorum/Dropout-NeuralNetworks

Dropout: A Simple Way to Prevent Neural Networks from Overfitting

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JuzerShakir/Logistic_Regression

A Mathematical Intuition behind Logistic Regression Algorithm

Size: 229 KB - Last synced: about 1 year ago - Pushed: over 2 years ago - Stars: 0 - Forks: 0

mli42/python_bootcamp

42AI Bootcamps - Python & Machine Learning

Language: Python - Size: 7.46 MB - Last synced: about 1 year ago - Pushed: over 1 year ago - Stars: 3 - Forks: 2

alkhdaniel/Elements-of-AI-Building-AI---Advanced

Elements of AI: Building AI - Advanced is an online course by Reaktor and University of Helsinki worth 2 ECTS.

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Mojtaba-Alehosseini/Machine-Learning

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mingzhang-yin/Meta-learning-without-memorization

A study on the following problems: what the memorization problem is in meta-learning; why memorization problem happens; and how we can prevent it. (ICLR 2020)

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david-adewoyin/machine_learning_basics

Plain Python Implementation of popular machine learning algorithms from scratch. Algorithms includes: Linear Regression, Logistic Regression, Softmax, Kmeans, Decision Tree,Bagging, Random Forest, etc.

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bmcmenamin/thresholdOut-explorations

playing with Dwork's adaptive holdout and how to use it for a grid-search

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gabrieldim/Baby-Health-Data-Science

Baby Health model made in Python.

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kyaiooiayk/Donts-in-Machine-Learning-Notes

Notes, tutorials, code snippets and templates focused on Don'ts for Machine Learning

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kimy-de/disturbvalue

DisturbValue: a regularization method for regressioin tasks

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AnuragSChatterjee/Introduction-To-Machine-Learning-In-Python-On-Kaggle

Pursued an Introductory Machine Learning course in Python on Kaggle in my free time, where I practiced on a dataset and built a small model on Kaggle.

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Foroozani/Machine-Learning

:construction: ML algorithms, employing different approaches

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kpetridis24/global-optimization

Unknown function approximation, given input-output measurements, using Genetic Algorithm

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pixel-debug/Machine-Learning

Códigos e enunciados utilizados no curso de Aprendizado de Máquina feito em 2020.

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vc1492a/us-state-under-over-fitting

A visual example of the concepts of under and overfitting in supervised machine learning using U.S. state border data.

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vvittis/MachineLearning

Size: 20 MB - Last synced: about 1 year ago - Pushed: over 2 years ago - Stars: 1 - Forks: 0

SourishW/ED-04-Sklearn-Notes

Machine learning with sklearn

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rpatrik96/lod-wmm-2019

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

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d4rthm4ul/ML-Overfitting-Lasso-and-Ridge-Regression

In this repository you will learn how to handle overfitting with the help of Lasso and Ridge Regression regularizations, also working mechanism of those while using useful charts.

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r-abishek/course_googleMLPracticum

Google course material on development of the state-of-the-art image classification model powering search in Google Photos

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Alessine/predicting_pirate_attack_success

Using machine learning to predict the success or failure of pirate attacks; elaborated during the Data Science Bootcamp at Propulsion Academy

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KentHsu/Manufacturing-Process-Modeling

Gaussian function basis regression overfitting location prediction

Language: Python - Size: 121 KB - Last synced: about 1 year ago - Pushed: about 3 years ago - Stars: 0 - Forks: 0

armanr99/NeuralNetwork

An implementation of neural network with regularization

Language: Jupyter Notebook - Size: 1010 KB - Last synced: about 1 year ago - Pushed: over 4 years ago - Stars: 1 - Forks: 0

alejandrods/Noise-Regularization-Method-Neural-Network

Adding noise as regularization method to reduce overffiting in neural networks

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TyMick/neural-net-tutorial-1

Following my first neural network tutorial

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sailfish009/self-adaptive-training Fork of LayneH/self-adaptive-training

Official implementation of the paper 'Self-Adaptive Training: beyond Empirical Risk Minimization'

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Develop-Packt/Solving-a-Classification-Problem-with-DNNs-Using-PyTorch

Make use of PyTorch's custom modules to define a network architecture and train a model. Investigate how to improve a model's performance and deploy your model for wider use.

Language: Jupyter Notebook - Size: 6.03 MB - Last synced: about 1 year ago - Pushed: about 4 years ago - Stars: 0 - Forks: 1

Develop-Packt/Creating-Ensemble-Models-with-Python

Recognize underfitting and overfitting, implement bagging and boosting, and build a stacked ensemble model using a number of classifiers.

Language: Jupyter Notebook - Size: 11.3 MB - Last synced: about 1 year ago - Pushed: about 4 years ago - Stars: 0 - Forks: 1

codeKk21/Classification-Optimization-technique

Fraud detection over twitter feed data

Language: MATLAB - Size: 873 KB - Last synced: about 1 year ago - Pushed: over 4 years ago - Stars: 1 - Forks: 0

gustavoisidio/avoidingoverfitting

Language: Jupyter Notebook - Size: 1.8 MB - Last synced: about 1 year ago - Pushed: over 4 years ago - Stars: 0 - Forks: 0

praxitelisk/Dont-Overfit-II

Kagglers have 20,000 rows of continuous variables, and a mere handful of training samples. Once again, we challenge you not to overfit. Do your best, model without overfitting, and add, perhaps, to your own legend.

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irfanalidv/Credit_Card_Fraud_Detection

Anonymized credit card transactions labeled as fraudulent or genuine

Language: R - Size: 4.88 KB - Last synced: about 1 year ago - Pushed: over 6 years ago - Stars: 0 - Forks: 0

hashtag32/Behavioral-Cloning

Udacity Self Driving Car Nanodegree - Behavioral Cloning

Language: Python - Size: 7.54 MB - Last synced: about 1 year ago - Pushed: almost 6 years ago - Stars: 0 - Forks: 0

miguelangelnieto/Predicting-Boston-Housing-Prices

Built a model to predict the value of a given house in the Boston real estate market using various statistical analysis tools. Identified the best price that a client can sell their house utilizing machine learning.

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dwallach1/ID3-Tree

ID3 decision tree implementation

Language: Python - Size: 430 KB - Last synced: about 1 year ago - Pushed: about 7 years ago - Stars: 0 - Forks: 1