Pinned Repositories
Awesome-DeepLearning-500FAQ
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。声明:所有内容来自(仅供学习):https://github.com/scutan90/DeepLearning-500-questions
Book3_Elements-of-Mathematics
Book_3_《数学要素》 | 鸢尾花书:从加减乘除到机器学习;本册有,583幅图,136个代码文件,其中24个Streamlit App;状态:清华社五审五校中;Github稿件基本稳定,欢迎提意见,会及时修改
Book6_First-Course-in-Data-Science
Book_6_《数据有道》 | 鸢尾花书:从加减乘除到机器学习;本册上传少部分草稿,2023年初会继续上传稿件。欢迎大家提意见哈,注意下载最新版本
DeepLearning
深度学习入门教程, 优秀文章, Deep Learning Tutorial
gdal
GDAL is an open source X/MIT licensed translator library for raster and vector geospatial data formats.
LeetCode-Py
⛽️「算法通关手册」,超详细的「算法与数据结构」基础讲解教程,「LeetCode」650+ 道题目 Python 版的详细解析。通过「算法理论学习」和「编程实战练习」相结合的方式,从零基础到彻底掌握算法知识。
PySpectrometer
Raspberry Pi Spectrometer
stac-overflow
Winners of the STAC Overflow: Map Floodwater from Radar Imagery competition
test
yoloair
🔥🔥🔥YOLOAir:Including YOLOv5, YOLOv7, Transformer, YOLOX, YOLOR and other networks... Support to improve backbone, head, loss, IoU, NMS...The original version was created based on YOLOv5
827597924's Repositories
827597924/Awesome-DeepLearning-500FAQ
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。声明:所有内容来自(仅供学习):https://github.com/scutan90/DeepLearning-500-questions
827597924/Book3_Elements-of-Mathematics
Book_3_《数学要素》 | 鸢尾花书:从加减乘除到机器学习;本册有,583幅图,136个代码文件,其中24个Streamlit App;状态:清华社五审五校中;Github稿件基本稳定,欢迎提意见,会及时修改
827597924/Book6_First-Course-in-Data-Science
Book_6_《数据有道》 | 鸢尾花书:从加减乘除到机器学习;本册上传少部分草稿,2023年初会继续上传稿件。欢迎大家提意见哈,注意下载最新版本
827597924/DeepLearning
深度学习入门教程, 优秀文章, Deep Learning Tutorial
827597924/gdal
GDAL is an open source X/MIT licensed translator library for raster and vector geospatial data formats.
827597924/LeetCode-Py
⛽️「算法通关手册」,超详细的「算法与数据结构」基础讲解教程,「LeetCode」650+ 道题目 Python 版的详细解析。通过「算法理论学习」和「编程实战练习」相结合的方式,从零基础到彻底掌握算法知识。
827597924/PySpectrometer
Raspberry Pi Spectrometer
827597924/stac-overflow
Winners of the STAC Overflow: Map Floodwater from Radar Imagery competition
827597924/test
827597924/yoloair
🔥🔥🔥YOLOAir:Including YOLOv5, YOLOv7, Transformer, YOLOX, YOLOR and other networks... Support to improve backbone, head, loss, IoU, NMS...The original version was created based on YOLOv5