Pinned Repositories
Advanced-Deep-Learning-with-Keras
Advanced Deep Learning with Keras, published by Packt
Appliances-energy-prediction-data
Data sets and scripts for the publication in Energy and Buildings Data driven prediction models of energy use of appliances in a low-energy house. Luis M. Candanedo, Véronique Feldheim, Dominique Deramaix. Energy and Buildings, Volume 140, 1 April 2017, Pages 81-97, ISSN 0378-7788,
arl-eegmodels
This is the Army Research Laboratory (ARL) EEGModels Project: A Collection of Convolutional Neural Network (CNN) models for EEG signal classification, using Keras and Tensorflow
Awesome-Deep-Learning-Resources
Rough list of my favorite deep learning resources, useful for revisiting topics or for reference. I have got through all of the content listed there, carefully. - Guillaume Chevalier
awesome_lightweight_networks
The implementation of various lightweight networks by using PyTorch. such as：MobileNetV2，MobileNeXt，GhostNet，ParNet，MobileViT、AdderNet，ShuffleNetV1-V2，LCNet，etc. ⭐⭐⭐⭐⭐
BCDU-Net
BCDU-Net : Medical Image Segmentation
BrainPad
Classification of EEG signals from the brain 🧠 through OpenBCI hardware and Tensorflow-Keras API
brats
DCML
Vehicle-Type-Classification-Using-CNN
Implement CNN models to classify vehicle type
yasinkaya1's Repositories
yasinkaya1/DCML
yasinkaya1/arl-eegmodels
This is the Army Research Laboratory (ARL) EEGModels Project: A Collection of Convolutional Neural Network (CNN) models for EEG signal classification, using Keras and Tensorflow
yasinkaya1/BCDU-Net
BCDU-Net : Medical Image Segmentation
yasinkaya1/brats
yasinkaya1/breast_mass_detection
End-to-end breast cancer detection in Python
yasinkaya1/CODES
Codes for some of my co-authored journal/conference papers
yasinkaya1/cs-video-courses
List of Computer Science courses with video lectures.
yasinkaya1/Feature-Distance-Loss
Discriminative Feature Learning through Feature Distance Loss
yasinkaya1/handson-ml
⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 instead.
yasinkaya1/handson-ml3
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
yasinkaya1/HEOA
The source code of human evolutionary optimization algorithm (HEOA)
yasinkaya1/keras-convnext-conversion
ConvNeXt conversion code for PT to TF along with evaluation code on ImageNet-1k val.
yasinkaya1/mafese
Feature Selection using Metaheuristics Made Easy: Open Source MAFESE Library in Python
yasinkaya1/mealpy
A collection of the state-of-the-art MEta-heuristic ALgorithms in PYthon (mealpy)
yasinkaya1/MetaFormer
A PyTorch implementation of "MetaFormer: A Unified Meta Framework for Fine-Grained Recognition". A reference PyTorch implementation of “CoAtNet: Marrying Convolution and Attention for All Data Sizes”
yasinkaya1/MGCA-RAFFNet
yasinkaya1/PMCNet
yasinkaya1/pycm
Multi-class confusion matrix library in Python
yasinkaya1/python-p2p-network
Framework to easily implement decentralized peer-to-peer network applications in Python
yasinkaya1/RAAGR2-Net
DL-Net: A Brain tumor Segmentation Network Using Parallel Processing of Multiple Spatial Frames
yasinkaya1/ResUNetPlusPlus-with-CRF-and-TTA
ResUNet++, CRF, and TTA for segmentation of medical images (IEEE JBIHI)
yasinkaya1/retina-unet
Retina blood vessel segmentation with a convolutional neural network
yasinkaya1/SAGA
yasinkaya1/Score-CAM
yasinkaya1/seriesnet
Time series prediction using dilated causal convolutional neural nets (temporal CNN)
yasinkaya1/TCACNet
A demo of MI-EEG classification using TCACNet
yasinkaya1/Toward-Sleep-Apnea-Detection-with-Lightweight-Multi-scaled-Fusion-Network
Toward Sleep Apnea Detection with Lightweight Multi-scaled Fusion Network
yasinkaya1/VGGIN-Net
VGGIN-Net architecture for imbalanced breast cancer classification
yasinkaya1/Wrapper-Feature-Selection-Toolbox-Python
This toolbox offers 13 wrapper feature selection methods (PSO, GA, GWO, HHO, BA, WOA, and etc.) with examples. It is simple and easy to implement.
yasinkaya1/WSL4MIS
Scribbles or Points-based weakly-supervised learning for medical image segmentation, a strong baseline, and tutorial for research and application.