Pinned Repositories
100-Days-Of-ML-Code
100-Days-Of-ML-Code中文版
2021-CV-Surveys
2021 年，计算机视觉相关综述。包括目标检测、跟踪........
Active-Learning-Papers
A list of papers on Active Learning and Uncertainty Estimation for Neural Networks.
Advance-Machine-Learning-Practical-Session-2
Online Passive-Aggressive Algorithms, Bandit Algorithm, Reinforcement Learning
Advanced-Deep-Learning-with-Keras
Advanced Deep Learning with Keras, published by Packt
algorithm-note
该系列包括数组，链表，树，图，递归，DP，有序表等相关数据结构与算法的讲解及代码实现。
algorithm-visualizer
:fireworks:Interactive Online Platform that Visualizes Algorithms from Code
ALiPy
ALiPy: Active Learning in Python is an active learning python toolbox, which allows users to conveniently evaluate, compare and analyze the performance of active learning methods.
alpf
Active Learning with Partial Feedback
approachingalmost
Approaching (Almost) Any Machine Learning Problem
kangzi's Repositories
kangzi/2021-CV-Surveys
2021 年，计算机视觉相关综述。包括目标检测、跟踪........
kangzi/algorithm-note
该系列包括数组，链表，树，图，递归，DP，有序表等相关数据结构与算法的讲解及代码实现。
kangzi/ALiPy
ALiPy: Active Learning in Python is an active learning python toolbox, which allows users to conveniently evaluate, compare and analyze the performance of active learning methods.
kangzi/awesome-computer-vision
A curated list of awesome computer vision resources
kangzi/awesome-deep-vision
A curated list of deep learning resources for computer vision
kangzi/best-of-ml-python
🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
kangzi/computer-vision-in-action
《计算机视觉实战演练：算法与应用》中文电子书、源码、读者交流社区（更新中，可以先 star）
kangzi/CVPR-2021-Papers
kangzi/data-science-learning-resources
A comprehensive list of free resources for learning data science
kangzi/dataset
医学影像数据集列表
kangzi/DeepLearningInMedicalImagingAndMedicalImageAnalysis
kangzi/doing_the_PhD
kangzi/eat_pytorch_in_20_days
Pytorch🍊🍉 is delicious, just eat it! 😋😋
kangzi/eat_tensorflow2_in_30_days
Tensorflow2.0 🍎🍊 is delicious, just eat it! 😋😋
kangzi/Efficient_Python_tricks_and_tools_for_data_scientists
Efficient Python Tricks and Tools for Data Scientists
kangzi/Learning-Multiclass-Classifier-Under-Noisy-Bandit-Feedback-Code
This algorithm is formulated to addresses the problem of multiclass classification with corrupted or noisy bandit feedback. In this setting, the learner maynot receive true feedback. Instead, it receives feedback that has beenflipped with some non-zero probability. We propose a novel approachto deal with noisy bandit feedback, based on the unbiased estimatortechnique. This algorithm can also efficiently estimate the noise rates, and thus providing an end-to-end framework. The proposed algorithm enjoys mistake bound of the order ofO(√T) in the highnoise case and of the order ofO(T^2/3) in the worst case.
kangzi/libact
Pool-based active learning in Python
kangzi/medical-datasets
tracking medical datasets, with a focus on medical imaging
kangzi/ml-study-plan
The Ultimate FREE Machine Learning Study Plan
kangzi/modAL
A modular active learning framework for Python
kangzi/MONAILabel
MONAI Label (Development in Progress)
kangzi/MRI-tumor-segmentation-Brats
MRI medical image segmentation
kangzi/OnlineLDS
Source code for the AAAI 2019 paper "On-Line Learning of Linear Dynamical Systems: Exponential Forgetting in Kalman Filters" (https://arxiv.org/abs/1809.05870)
kangzi/pytorch_active_learning
PyTorch Library for Active Learning to accompany Human-in-the-Loop Machine Learning book
kangzi/SegLoss
A collection of loss functions for medical image segmentation
kangzi/t81_558_deep_learning
Washington University (in St. Louis) Course T81-558: Applications of Deep Neural Networks
kangzi/TransUNet
This repository includes the official project of TransUNet, presented in our paper: TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.
kangzi/UNetPlusPlus
Official Keras Implementation for UNet++ in IEEE Transactions on Medical Imaging and DLMIA 2018
kangzi/YesPlayMusic
高颜值的第三方网易云播放器，支持 Windows / macOS / Linux :electron:
kangzi/yt-channels-DS-AI-ML-CS
A comprehensive list of 150+ YouTube Channels for Data Science, Data Engineering, Machine Learning, Deep learning, Computer Science, programming, software engineering, etc.