bobnju Goto Github PK

ailearning

AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP

animegan2-pytorch

PyTorch implementation of AnimeGANv2

awesome-earthobservation-code

A curated list of awesome tools, tutorials, code, helpful projects, links, stuff about Earth Observation and Geospatial stuff!

awesome-gee

A curated list of Google Earth Engine resources

awesome-machine-learning-cn

机器学习资源大全中文版，包括机器学习领域的框架、库以及软件

awesome-python

A curated list of awesome Python frameworks, libraries, software and resources

awesome-python-cn

Python资源大全中文版，包括：Web框架、网络爬虫、模板引擎、数据库、数据可视化、图片处理等，由伯乐在线持续更新。

chinese-poetry

The most comprehensive database of Chinese poetry 🧶最全中华古诗词数据库, 唐宋两朝近一万四千古诗人, 接近5.5万首唐诗加26万宋诗. 两宋时期1564位词人，21050首词。

cs-notes

:books: Tech Interview Guide 技术面试必备基础知识、Leetcode 题解、Java、C++、Python、后端面试、操作系统、计算机网络、系统设计

d3-china-map

A template to create map of China by province using d3js

deep-learning-with-pytorch-tutorials

深度学习与PyTorch入门实战视频教程 配套源代码和PPT

deeplearning

深度学习入门教程&&优秀文章&&Deep Learning Tutorial

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17个章节，20多万字。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[email protected] 版权所有，违权必究 Tan 2018.06

dl-for-satellite-image-analysis

This includes short and minimalistic few examples covering fundamentals of Deep Learning for Satellite Image Analysis (Remote Sensing).

dump

early-earth-carbon-cycle

Python source code for geological carbon cycle model from Krissansen-Totton et al. (2018), PNAS.

forum

18-day-web

front-end-note

:memo: Web 前端笔记

git-study

a doce of git study

gitskills

haoel.github.io

hello-world

This is my first Git Hub repository:hello-wrorld in NJU 09 18(th),2017 01:23 PM

introduciton-2-ml-with-python-notebook

:notebook: Python机器学习基础教程中文Notebook

learngit

lihang-code

《统计学习方法》的代码实现

machine-learning

:zap:机器学习实战（Python3）：kNN、决策树、贝叶斯、逻辑回归、SVM、线性回归、树回归

machinelearning

Machine learning resources

ml

modality_tests

Various python tests in to identify and describe multi-modal data distributions.

my-first-blog

my-first-blog