XueYang6

protease_activity_analysis

Python toolkit and package for analyzing enzyme activity data

survshap

SurvSHAP(t): Time-dependent explanations of machine learning survival models

RTSGAN

pytorch-cifar100

Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet, NasNet, Residual Attention Network, SENet, WideResNet)

pykan

Kolmogorov Arnold Networks

TimeGAN

Codebase for Time-series Generative Adversarial Networks (TimeGAN) - NeurIPS 2019

recommenders

Best Practices on Recommendation Systems

zennit-crp

An eXplainable AI toolkit with Concept Relevance Propagation and Relevance Maximization

datasets

Datasets used in Plotly examples and documentation

grok-1

Grok open release

An-Interpretable-Deep-Learning-Approach-for-Skin-Cancer-Categorization-IEEE2023

Multiclass skin cancer detection using explainable AI for checking the models' robustness

keras

Deep Learning for humans

screenshot-to-code

Drop in a screenshot and convert it to clean code (HTML/Tailwind/React/Vue)

generative-models

Generative Models by Stability AI

EfficientNet-PyTorch

A PyTorch implementation of EfficientNet

Data_Augmentation_Zoo_for_Object_Detection

Includes: Learning data augmentation strategies for object detection | GridMask data augmentation | Augmentation for small object detection in Numpy. Use RetinaNet with ResNet-18 to test these methods on VOC and KITTI.

deep-learning-for-image-processing

deep learning for image processing including classification and object-detection etc.

Pytorch-UNet

PyTorch implementation of the U-Net for image semantic segmentation with high quality images

Unet-Segmentation-Pytorch-Nest-of-Unets

Implementation of different kinds of Unet Models for Image Segmentation - Unet , RCNN-Unet, Attention Unet, RCNN-Attention Unet, Nested Unet

Tracking-Anything-with-DEVA

[ICCV 2023] Tracking Anything with Decoupled Video Segmentation

ddbourgin-numpy-ml

saliency

Framework-agnostic implementation for state-of-the-art saliency methods (XRAI, BlurIG, SmoothGrad, and more).

shap

A game theoretic approach to explain the output of any machine learning model.

lime

Lime: Explaining the predictions of any machine learning classifier

Android_Hospital_Body_Master

pytorch-deep-learning

Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.

Track-Anything

Track-Anything is a flexible and interactive tool for video object tracking and segmentation, based on Segment Anything, XMem, and E2FGVI.

d2l-zh

《动手学深度学习》：面向中文读者、能运行、可讨论。中英文版被70多个国家的500多所大学用于教学。

Dive-into-DL-TensorFlow2.0

本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为TensorFlow 2.0实现，项目已得到李沐老师的认可

reference

为开发人员分享快速参考备忘清单(速查表)