residual-learning

Here are 36 public repositories matching this topic...

RahulSkr / skinCarcinomaDetection

A deep neural network developed following the residual learning and separable convolution paradigms to diagnose basal and squamous cell carcinoma using a subset of ISIC dataset.

python deep-neural-networks dataset publication residual-learning separable-convolutions separable-convolution-paradigms

Updated Jan 27, 2019
Python

Meteors-flash / Research-and-Application-on-Single-Image-Super-Resolution

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a graduation project

pytorch tkinter residual-learning subpixel-convolution imagesuperresolution

Updated Aug 23, 2021
Python

shawnhan108 / The-Recommenders

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An exploration of recommender systems using Bayesian Bandit, matrix factorization, deep learning, and residual learning.

deep-learning collaborative-filtering matrix-factorization recommender-system movielens-dataset residual-learning multilayer-networks bayesian-bandit

Updated Aug 31, 2020
Jupyter Notebook

MahdiTabassian / Deep-Clutter-Filtering

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Deep Spatiotemporal Clutter Filtering of Transthoracic Echocardiographic Images Using a 3D Convolutional Auto-Encoder

attention-mechanism residual-learning spatiotemporal-clutter-filtering 3d-convolutional-auto-encoder transthoracic-echocardiography

Updated Jan 27, 2024
Python

shreyansh-2003 / MLR-Gradient-Descent-For-Model-Explainability

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This repository contains a comprehensive implementation of gradient descent for linear regression, including visualizations and comparisons with ordinary least squares (OLS) regression. It also includes an additional implementation for multiple linear regression using gradient descent.

machine-learning linear-regression data-visualization data-analysis gradient-descent statsmodels optimization-algorithms boston-housing-price-prediction residual-learning sklearn-library ordinary-least-squares explainable-machine-learning multiple-linear-regression-model

Updated May 28, 2023
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deep-learning-algorithm / ResNet

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Implementation of ResNet series Algorithm

pytorch resnet residual-network residual-learning resnet-50 resnet-18 resnet-34 resnet-101 resnet-152 densetnet densetnet-121 densetnet-169 densenet-201 densenet-264

Updated Jun 22, 2022
Python

tanishqjasoria / DeepLearning

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Implementation of some Neural Network architecture using Numpy, TensorFlow and Keras.

deep-neural-networks deep-learning tensorflow convolutional-neural-networks transfer-learning residual-learning

Updated Nov 13, 2018
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tate8 / AI-flashcards

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An educational AI-powered flashcards web app

javascript css python heroku html flask website machine-learning ai neural-network tensorflow keras cnn image-classification residual-learning

Updated Jan 23, 2023
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GSaiDheeraj / deepfakesdetection

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Classifying Deepfake imges using Residual learning Architecture

css html deep-learning js keras image-processing python3 nvidia convolutional-neural-networks residual-networks residual-learning mtcnn mtcnn-face-detection inception-resnet-v2 gradcam deepfakes vggface2 tensorflow2 deepfake-detection

Updated Jun 7, 2022
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Chandra-cc / Pneumonia-diagnosis-using-deep-residual-learning-with-a-classification-accuracy-of-98.22

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Classification between normal and pneumonia affected chest-X-ray images using deep residual learning along with separable convolutional network(CNN). This methodology involves efficient edge preservation and image contrast enhancement techniques for better classification of the X-ray images.

keras convolutional-networks chest-xray-images deep-residual-learning keras-neural-networks residual-learning dense pneumonia separable-convolutions separable-convolution-paradigms max-poo

Updated Aug 8, 2019
Jupyter Notebook

delongsh / RLECC

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Face Template Protection Through Residual Learning Based Error-Correctinig Codes

biometrics residual-learning face-template-protection

Updated Apr 11, 2021
Python

Chandra-cc / IDCBreastCancer_histopathologyImages_deepResidualLearning

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IDC prediction in breast cancer histopathology images using deep residual learning with an accuracy of 99.37% in a subset of images containing a total of 7,500 microscopic images.