LCJHust's Stars
CyC2018/CS-Notes
:books: 技术面试必备基础知识、Leetcode、计算机操作系统、计算机网络、系统设计
labuladong/fucking-algorithm
刷算法全靠套路,认准 labuladong 就够了!English version supported! Crack LeetCode, not only how, but also why.
tensorflow/models
Models and examples built with TensorFlow
MisterBooo/LeetCodeAnimation
Demonstrate all the questions on LeetCode in the form of animation.(用动画的形式呈现解LeetCode题目的思路)
scutan90/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18个章节,50余万字。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............ 如有意合作,联系scutjy2015@163.com 版权所有,违权必究 Tan 2018.06
google-research/google-research
Google Research
PaddlePaddle/PaddleClas
A treasure chest for visual classification and recognition powered by PaddlePaddle
d2l-ai/berkeley-stat-157
Homepage for STAT 157 at UC Berkeley
Sophia-11/Machine-Learning-Notes
周志华《机器学习》手推笔记
shelhamer/fcn.berkeleyvision.org
Fully Convolutional Networks for Semantic Segmentation by Jonathan Long*, Evan Shelhamer*, and Trevor Darrell. CVPR 2015 and PAMI 2016.
QingyongHu/SoTA-Point-Cloud
🔥[IEEE TPAMI 2020] Deep Learning for 3D Point Clouds: A Survey
Thinkgamer/books
技术资料分享
erikwijmans/Pointnet2_PyTorch
PyTorch implementation of Pointnet2/Pointnet++
QingyongHu/RandLA-Net
🔥RandLA-Net in Tensorflow (CVPR 2020, Oral & IEEE TPAMI 2021)
TRI-ML/packnet-sfm
TRI-ML Monocular Depth Estimation Repository
dongdonghy/Detection-PyTorch-Notebook
代码 -《深度学习之PyTorch物体检测实战》
fuweifu-vtoo/Semantic-segmentation
采用Pytorch的入门语义分割项目,支持的网络有Unet和Segnet;遥感语义分割;Unet;Segnet;Remote sensing semantic segmentation;
tkuanlun350/Tensorflow-SegNet
Implement slightly different caffe-segnet in tensorflow
rishizek/tensorflow-deeplab-v3
DeepLabv3 built in TensorFlow
cxy1997/3D_adapt_auto_driving
Train in Germany, Test in The USA: Making 3D Object Detectors Generalize
miraclewkf/MXNet-Deep-Learning-in-Action
hyu-cvlab/sweepnet
Changhee Won, Jongbin Ryu and Jongwoo Lim "SweepNet: Wide-baseline Omnidirectional Depth Estimation", in ICRA 2019
cuicp/Algorithm_Interview_Notes-Chinese
2018/2019/校招/春招/秋招/算法/机器学习(Machine Learning)/深度学习(Deep Learning)/自然语言处理(NLP)/C/C++/Python/面试笔记