Pinned Repositories
2D-Morphological-Network
2D Morphological-Network
Arduino_Learning
Arduino practices and projects.
AttnSleep
The code for TNSRE work: "An Attention-based Deep Learning Approach for Sleep Stage Classification with Single-Channel EEG"
awesome-data-labeling
A curated list of awesome data labeling tools
awesome-object-detection
Awesome Object Detection based on handong1587 github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html
caoqi95.github.io
personal blog pages
CV_Learning
Projects related to computer vision and image processing.
DeepLearning-500-questions
深度学习500问,以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述,以帮助自己及有需要的读者。 全书分为15个章节,近20万字。由于水平有限,书中不妥之处恳请广大读者批评指正。 未完待续............ 版权所有,违权必究 Tan 2018.06
Digit_Recognition
Digits recognition for Data Mining course.
Tools-list
Tools collection.
caoqi95's Repositories
caoqi95/DeepLearning-500-questions
深度学习500问,以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述,以帮助自己及有需要的读者。 全书分为15个章节,近20万字。由于水平有限,书中不妥之处恳请广大读者批评指正。 未完待续............ 版权所有,违权必究 Tan 2018.06
caoqi95/Digit_Recognition
Digits recognition for Data Mining course.
caoqi95/cardiac-segmentation
Right Ventricle Cardiac MRI Segmentation
caoqi95/cn-deep-learning
https://cn.udacity.com/course/deep-learning-nanodegree-foundation--nd101/
caoqi95/CS-Notes
:books: Computer Science Learning Notes
caoqi95/cs231n.github.io
Public facing notes page
caoqi95/Data-Competition-TopSolution
Data competition Top Solution 数据竞赛top解决方案开源整理
caoqi95/Deep-Learning-Papers-Reading-Roadmap
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
caoqi95/depression-detect
Predicting depression from acoustic features of speech using a Convolutional Neural Network.
caoqi95/document-style-guide
中文技术文档的写作规范
caoqi95/maxout-pytorch
notebook demonstrating a maxout net on MNIST
caoqi95/PRMLT
Matlab code for machine learning algorithms in book PRML
caoqi95/research-method
论文写作与资料分享
caoqi95/Semantic-Segmentation-Suite
Semantic Segmentation Suite in TensorFlow. Implement, train, and test new Semantic Segmentation models easily!