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https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#deeplearning-tutorial
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#一-入门资料
完备的 AI 学习路线，最详细的中英文资源整理https://zhuanlan.zhihu.com/p/64052743
AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLhttps://github.com/apachecn/AiLearning
Machine-Learninghttps://github.com/shunliz/Machine-Learning
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#数学基础
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning/blob/master/notes/Images/MathematicalBasis.png
矩阵微积分https://zh.wikipedia.org/wiki/%E7%9F%A9%E9%98%B5%E5%BE%AE%E7%A7%AF%E5%88%86
机器学习的数学基础https://github.com/fengdu78/Data-Science-Notes/tree/master/0.math/0.basic
CS229线性代数与概率论基础https://github.com/fengdu78/Data-Science-Notes/tree/master/0.math/1.CS229
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#机器学习基础
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#快速入门
机器学习算法地图http://www.tensorinfinity.com/paper_18.html
机器学习 吴恩达 Coursera个人笔记https://github.com/Mikoto10032/DeepLearning/blob/master/books/%5BML-Coursera%5D%5B2014%5D%5BAndrew%20Ng%5D/%5B2014%5D%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E4%B8%AA%E4%BA%BA%E7%AC%94%E8%AE%B0%E5%AE%8C%E6%95%B4%E7%89%88v5.1.pdf
视频（含官方笔记）https://www.coursera.org/learn/machine-learning
CS229 课程讲义中文翻译https://kivy-cn.github.io/Stanford-CS-229-CN/#/
机器学习 吴恩达 cs229个人笔记https://github.com/Mikoto10032/DeepLearning/blob/master/books/%5BML-CS229%5D%5B2011%5D%5BAndrew%20NG%5D/%5B2011%5D%E6%96%AF%E5%9D%A6%E7%A6%8F%E5%A4%A7%E5%AD%A6%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%AF%BE%E7%A8%8B%E4%B8%AA%E4%BA%BA%E7%AC%94.pdf
官网（笔记）http://cs229.stanford.edu/
视频（中文字幕）http://open.163.com/newview/movie/free?pid=M6SGF6VB4&mid=M6SGHFBMC
百页机器学习http://themlbook.com/wiki/doku.php
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#深入理解
《统计学习方法》李航https://github.com/Mikoto10032/DeepLearning/tree/master/books/%E6%9D%8E%E8%88%AA-%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0
《统计学习方法》各章节笔记https://www.cnblogs.com/YongSun/tag/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/
《统计学习方法》各章节笔记https://zhuanlan.zhihu.com/c_1213397558586257408
推荐答案：statistical-learning-method-solutions-manualhttps://github.com/datawhalechina/statistical-learning-method-solutions-manual
《统计学习方法》各章节笔记https://www.cnblogs.com/liaohuiqiang/category/1039314.html
《统计学习方法》各章节代码实现与课后习题参考解答https://blog.csdn.net/breeze_blows/article/details/85469944
《模式识别与机器学习》 Christopher Bishophttps://github.com/Mikoto10032/DeepLearning/blob/master/books/%E6%A8%A1%E5%BC%8F%E8%AF%86%E5%88%AB%E4%B8%8E%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0PRML_Chinese_vision.pdf
《机器学习》 周志华https://github.com/Mikoto10032/DeepLearning/blob/master/books/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%91%A8%E5%BF%97%E5%8D%8E.pdf
南瓜书：pumpkin-bookhttps://github.com/datawhalechina/pumpkin-book
《机器学习实战》 PelerHarringtonhttps://github.com/Mikoto10032/DeepLearning/blob/master/books/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%AE%9E%E6%88%98%20%E4%B8%AD%E6%96%87%E5%8F%8C%E9%A1%B5%E7%89%88.pdf
机器学习与深度学习书单https://mp.weixin.qq.com/s?__biz=MzAxMjcyNjE5MQ==&mid=2650488718&idx=1&sn=815a79d27d500f0fb8db1fe1fc6cfe48&chksm=83a2e54eb4d56c58a0989654f920d64ad2784ce52e4b2bc6883974257cf475c9983f05fb88c1&scene=0&xtrack=1&ascene=14&devicetype=android-28&version=27000339&nettype=WIFI&abtest_cookie=AwABAAoACwATAAQAI5ceAFaZHgDQmR4A3JkeAAAA&lang=zh_CN&pass_ticket=oEB1108Pes6HkdxEITmBjTb2Glju5%2BEGqHZKz50fMg0rgK4l9Fodlbe%2FDm96iX57&wx_header=1
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#深度学习基础
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#快速入门-1
深度学习思维导图https://github.com/dformoso/deeplearning-mindmap
深度学习算法地图http://www.tensorinfinity.com/paper_158.html
《斯坦福大学深度学习基础教程》 Andrew Ng（吴恩达）https://github.com/Mikoto10032/DeepLearning/blob/master/books/%E6%96%AF%E5%9D%A6%E7%A6%8F%E5%A4%A7%E5%AD%A6-%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E5%9F%BA%E7%A1%80%E6%95%99%E7%A8%8B.pdf
深度学习 吴恩达 个人笔记http://www.ai-start.com/dl2017/
视频http://mooc.study.163.com/smartSpec/detail/1001319001.htm
MIT深度学习基础-2019视频课程https://deeplearning.mit.edu/
台湾大学（NTU）李宏毅教授课程http://speech.ee.ntu.edu.tw/~tlkagk/index.html
leeml-noteshttps://github.com/datawhalechina/leeml-notes
图解深度学习_Grokking-Deep-Learninghttps://github.com/iamtrask/Grokking-Deep-Learning
《神经网络与深度学习》 Michael Nielsenhttps://github.com/Mikoto10032/DeepLearning/blob/master/books/%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E5%92%8C%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0neural%20networks%20and%20deep-learning-%E4%B8%AD%E6%96%87_ALL.pdf
CS321-Hintonhttp://www.cs.toronto.edu/~tijmen/csc321/
CS230: Deep Learninghttps://web.stanford.edu/class/cs230/
CS294-112http://rail.eecs.berkeley.edu/deeprlcourse/resources/
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#计算机视觉
CS231 李飞飞 已授权个人翻译笔记https://zhuanlan.zhihu.com/p/21930884
视频http://study.163.com/course/courseMain.htm?courseId=1003223001
计算机视觉研究方向https://mp.weixin.qq.com/s/WNkzfvYtEO5zJoe_-yAPow
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#自然语言处理
CS224n: Natural Language Processing with Deep Learninghttp://web.stanford.edu/class/cs224n/index.html
NLP上手教程https://github.com/FudanNLP/nlp-beginner
NLP入门推荐书目（2019版）https://zhuanlan.zhihu.com/p/58874484
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#深度强化学习
CS234: Reinforcement Learninghttp://web.stanford.edu/class/cs234/index.html
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#深入理解-1
《深度学习》 Yoshua Bengio.Ian GoodFellowhttps://github.com/Mikoto10032/DeepLearning/blob/master/books/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0.Yoshua%20Bengio%2BIan%20GoodFellow.pdf
《自然语言处理》Jacob Eisensteinhttps://github.com/Mikoto10032/DeepLearning/blob/master/books/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86.Jacob%20Eisenstein.pdf
《强化学习》https://github.com/Mikoto10032/DeepLearning/blob/master/books/Reinforcement%20Learning.Sutton.pdf
第二版http://incompleteideas.net/book/RLbook2018trimmed.pdf
hangdong的深度学习博客,论文推荐https://handong1587.github.io/categories.html#deep_learning-ref
Practical Deep Learning for Coders, v3https://course.fast.ai/
《Tensorflow实战Google深度学习框架》 郑泽宇 顾思宇https://github.com/Mikoto10032/DeepLearning/blob/master/books/Tensorflow%20%E5%AE%9E%E6%88%98Google%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E6%A1%86%E6%9E%B6.pdf
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#一些书单
2019年最新-深度学习、生成对抗、Pytorch优秀教材推荐https://zhuanlan.zhihu.com/p/63784033
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#工程能力
https://camo.githubusercontent.com/58d36614c20e3b9ef5ad2d43c9188de16ca86926eb15c478b42a364b628497c6/68747470733a2f2f706963342e7a68696d672e636f6d2f76322d30303930313332373836383866353230633037306232373931303235356362315f722e6a7067
如何系统地学习算法？https://www.zhihu.com/question/20588261/answer/798928056
LeetCodehttps://leetcode.com/
leetcode题解https://github.com/azl397985856/leetcode
《算法导论》中算法的C++实现https://github.com/huaxz1986/cplusplus-_Implementation_Of_Introduction_to_Algorithms
机器学习算法实战https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%AE%9E%E6%88%98%E7%AF%87
深度学习框架https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E6%A1%86%E6%9E%B6
如何成为一名算法工程师https://mp.weixin.qq.com/s/YMtnBAVDZepsMTO4h-VRtQ
从小白到入门算法，我的经验分享给你～https://mp.weixin.qq.com/s?__biz=MzAxMjcyNjE5MQ==&mid=2650488786&idx=1&sn=68b9536d0b0b3105ab8d79f8efcb0a4b&chksm=83a2e512b4d56c045c6ab0349108842e6a5b26e8f3e507ff5d19ee50e3bd63ef149a36d23eef&scene=0&xtrack=1&ascene=14&devicetype=android-28&version=27000437&nettype=WIFI&abtest_cookie=BAABAAoACwASABMABgAjlx4AVpkeANCZHgDcmR4A8ZkeAAOaHgAAAA%3D%3D&lang=zh_CN&pass_ticket=4yovfEr0v09yZCvvQ1NEy12qGIonnRpGi774X09Mh5EZD2oL%2BRz6FTtX9R5gALB1&wx_header=1
我的研究生这三年https://zhuanlan.zhihu.com/p/54161673
编程面试的题目分类https://zhuanlan.zhihu.com/p/89392459
《AI算法工程师手册》http://www.huaxiaozhuan.com/
如何准备算法工程师面试，斩获一线互联网公司机器学习岗offer？https://zhuanlan.zhihu.com/p/76827460
【完结】深度学习CV算法工程师从入门到初级面试有多远，大概是25篇文章的距离https://mp.weixin.qq.com/s/HZ3Cd2jHuikyFN9ydvcMTw
计算机相关技术面试必备https://github.com/CyC2018/CS-Notes
CS-WiKihttps://veal98.gitee.io/cs-wiki/#/
计算机基础面试问题全面总结https://github.com/wolverinn/Waking-Up
TeachYourselfCS-CNhttps://github.com/keithnull/TeachYourselfCS-CN
面试算法笔记-中文https://github.com/imhuay/Algorithm_for_Interview-Chinese
算法工程师面试https://github.com/DarLiner/Algorithm_Interview_Notes-Chinese
深度学习面试题目https://github.com/ShanghaiTechAIClub/DLInterview
深度学习500问https://github.com/scutan90/DeepLearning-500-questions
AI算法岗求职攻略https://github.com/amusi/AI-Job-Notes#Strategy
Kaggle实战https://patch-diff.githubusercontent.com/UHT2020/DeepLearning/blob/master
kaggle竞赛宝典第一章-竞赛框架篇！:star:https://mp.weixin.qq.com/s/EGiFG6u9BYr1aBdq0a0wIQ
Kaggle 项目实战（教程） = 文档 + 代码 + 视频https://github.com/apachecn/kaggle
Kaggle入门系列：（一）机器学习环境搭建https://zhuanlan.zhihu.com/p/29086448
Kaggle入门系列：（二）Kaggle简介https://zhuanlan.zhihu.com/p/29417603
Kaggle入门系列（三）Titanic初试身手https://zhuanlan.zhihu.com/p/29086614
从 0 到 1 走进 Kagglehttps://zhuanlan.zhihu.com/p/61660061
Kaggle 入门指南https://zhuanlan.zhihu.com/p/25742261
一个框架解决几乎所有机器学习问题https://zhuanlan.zhihu.com/p/61657532
Approaching (Almost) Any Machine Learning Problem | Abhishek Thakurhttp://blog.kaggle.com/2016/07/21/approaching-almost-any-machine-learning-problem-abhishek-thakur/
分分钟带你杀入Kaggle Top 1%https://zhuanlan.zhihu.com/p/27424282
如何达到Kaggle竞赛top 2%？这里有一篇特征探索经验帖https://zhuanlan.zhihu.com/p/48758045
如何在 Kaggle 首战中进入前 10%？https://zhuanlan.zhihu.com/p/27486736
Kaggle 首战 Top 2%, APTOS 2019 复盘总结 + 机器学习竞赛通用流程归纳http://bbs.cvmart.net/topics/1717
kaggle的riiid比赛里关于数据处理时间空间优化的笔记https://zhuanlan.zhihu.com/p/344388290
大数据&机器学习相关竞赛推荐https://blog.csdn.net/weixin_33739541/article/details/87565983
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#二-神经网络模型概览
1. 一文看懂25个神经网络模型https://blog.csdn.net/qq_35082030/article/details/73368962
2. DNN概述论文：详解前馈、卷积和循环神经网络技术https://zhuanlan.zhihu.com/p/29141828
3. colah's bloghttp://colah.github.io/
4. Model Zoomhttps://modelzoo.co/
5. DNN概述https://zhuanlan.zhihu.com/p/29141828
GitHub上的机器学习/深度学习综述项目合集https://zhuanlan.zhihu.com/p/60245227
AlphaTree-graphic-deep-neural-networkhttps://github.com/weslynn/AlphaTree-graphic-deep-neural-network
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#cnn
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#发展史
94页论文综述卷积神经网络：从基础技术到研究前景https://zhuanlan.zhihu.com/p/35388569
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#图像分类
从LeNet-5到DenseNethttps://zhuanlan.zhihu.com/p/31006686
深度学习笔记（十一）网络 Inception, Xception, MobileNet, ShuffeNet, ResNeXt, SqueezeNet, EfficientNet, MixConvhttps://www.cnblogs.com/xuanyuyt/p/11329998.html
CNN网络结构的发展https://zhuanlan.zhihu.com/p/68411179
Awesome - Image Classification：论文&&代码大全https://github.com/weiaicunzai/awesome-image-classification
pytorch-image-modelshttps://github.com/rwightman/pytorch-image-models
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#目标检测
深度学习之目标检测的前世今生（Mask R-CNN）https://zhuanlan.zhihu.com/p/32830206
深度学习目标检测模型全面综述：Faster R-CNN、R-FCN和SSDhttps://zhuanlan.zhihu.com/p/29434605
从RCNN到SSD，这应该是最全的一份目标检测算法盘点https://zhuanlan.zhihu.com/p/36184131
目标检测算法综述三部曲https://zhuanlan.zhihu.com/p/40047760
基于深度学习的目标检测算法综述（一）https://zhuanlan.zhihu.com/p/40047760
基于深度学习的目标检测算法综述（二）https://zhuanlan.zhihu.com/p/40020809
基于深度学习的目标检测算法综述（三）https://zhuanlan.zhihu.com/p/40102001
From RCNN to YOLOv3https://patch-diff.githubusercontent.com/UHT2020/DeepLearning/blob/master
上https://zhuanlan.zhihu.com/p/35724768
下https://zhuanlan.zhihu.com/p/35731743
后 R-CNN时代， Faster R-CNN、SSD、YOLO 各类变体统治下的目标检测综述：Faster R-CNN系列胜了吗？https://zhuanlan.zhihu.com/p/38709522
目标检测进化史https://zhuanlan.zhihu.com/p/60590369
CVPR2019目标检测方法进展综述https://zhuanlan.zhihu.com/p/59376548
一文看尽21篇目标检测最新论文（腾讯/Google/商汤/旷视/清华/浙大/CMU/华科/中科院等https://zhuanlan.zhihu.com/p/61080508
我这两年的目标检测https://zhuanlan.zhihu.com/p/82491218
Anchor-Free目标检测算法https://patch-diff.githubusercontent.com/UHT2020/DeepLearning/blob/master
第一篇：arxiv2015_baidu_DenseBoxhttps://zhuanlan.zhihu.com/p/40221183
如何评价最新的anchor-free目标检测模型FoveaBox？https://www.zhihu.com/question/319605567/answer/647844997
FCOS: 最新的one-stage逐像素目标检测算法https://zhuanlan.zhihu.com/p/61644900
最新的Anchor-Free目标检测模型FCOS，现已开源！https://zhuanlan.zhihu.com/p/62198865
中科院牛津华为诺亚提出CenterNet，one-stage detector可达47AP，已开源！https://zhuanlan.zhihu.com/p/62789701
AnchorFreeDetectionhttps://github.com/VCBE123/AnchorFreeDetection
Anchor free深度学习的目标检测方法https://zhuanlan.zhihu.com/p/64563186
聊聊Anchor的"前世今生"（上）https://zhuanlan.zhihu.com/p/63273342
聊聊Anchor的"前世今生"（下）https://zhuanlan.zhihu.com/p/68291859
目标检测算法综述之FPN优化篇https://zhuanlan.zhihu.com/p/62975854
一文看尽物体检测中的各种FPNhttps://zhuanlan.zhihu.com/p/148738276
awesome-object-detection：论文&&代码https://github.com/amusi/awesome-object-detection
deep_learning_object_detectionhttps://github.com/hoya012/deep_learning_object_detection
ObjectDetectionImbalancehttps://github.com/kemaloksuz/ObjectDetectionImbalance
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#图像分割语义分割实例分割全景分割
图像语义分割(Semantic segmentation) Surveyhttps://zhuanlan.zhihu.com/p/36801104
干货 | 一文概览主要语义分割网络https://blog.csdn.net/qq_20084101/article/details/80432960
语义分割 发展综述https://zhuanlan.zhihu.com/p/37618829
9102年了，语义分割的入坑指南和最新进展都是什么样的https://zhuanlan.zhihu.com/p/76603228
实例分割最新最全面综述：从Mask R-CNN到BlendMaskhttps://zhuanlan.zhihu.com/p/110132002
语义分割综述：深度学习背景下的语义分割的发展状况【推荐】https://zhuanlan.zhihu.com/p/133212654
Awesome Semantic Segmentation：论文&&代码https://github.com/mrgloom/awesome-semantic-segmentation
一篇看完就懂的最新语义分割综述https://zhuanlan.zhihu.com/p/110123136
基于深度学习的语义分割综述https://zhuanlan.zhihu.com/p/142451150
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#轻量化卷积神经网络
纵览轻量化卷积神经网络：SqueezeNet、MobileNet、ShuffleNet、Xceptionhttps://zhuanlan.zhihu.com/p/32746221
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#人脸相关
如何走近深度学习人脸识别？你需要这篇超长综述 | 附开源代码https://zhuanlan.zhihu.com/p/35295839
人脸检测和识别算法综述https://patch-diff.githubusercontent.com/UHT2020/DeepLearning/blob/master
人脸检测算法综述 https://zhuanlan.zhihu.com/p/36621308
人脸检测背景介绍和发展现状https://zhuanlan.zhihu.com/p/32702868
人脸识别算法演化史https://zhuanlan.zhihu.com/p/36416906
CascadeCNNhttps://blog.csdn.net/shuzfan/article/details/50358809
MTCNNhttps://blog.csdn.net/qq_14845119/article/details/52680940
awesome-Face_Recognitionhttps://github.com/ChanChiChoi/awesome-Face_Recognition
异质人脸识别研究综述https://zhuanlan.zhihu.com/p/64191484
老板来了：人脸识别+手机推送，老板来了你立刻知道。https://zhuanlan.zhihu.com/p/26431250
手把手教你用Python实现人脸识别https://zhuanlan.zhihu.com/p/33456076
人脸识别项目，网络模型，损失函数，数据集相关总结https://www.jianshu.com/p/e57205edc364
基于深度学习的人脸识别技术综述https://zhuanlan.zhihu.com/p/24816781
如何走近深度学习人脸识别？你需要这篇超长综述https://zhuanlan.zhihu.com/p/35295839
人脸识别损失函数综述（附开源实现）https://zhuanlan.zhihu.com/p/51324547
Face Recognition Loss on Mnist with Pytorchhttps://zhuanlan.zhihu.com/p/64427565
人脸识别的LOSS（上）https://zhuanlan.zhihu.com/p/34404607
人脸识别的LOSS（下）https://zhuanlan.zhihu.com/p/34436551
人脸关键点检测https://patch-diff.githubusercontent.com/UHT2020/DeepLearning/blob/master
【每周CV论文推荐】 初学深度学习人脸关键点检测必读文章https://zhuanlan.zhihu.com/p/88344339
从传统方法到深度学习，人脸关键点检测方法综述https://mp.weixin.qq.com/s/CvdeV5xgUF0kStJQdRst0w
人脸关键点检测综述https://zhuanlan.zhihu.com/p/42968117
人脸专集4 | 遮挡、光照等因素的人脸关键点检测https://zhuanlan.zhihu.com/p/62824113
【Face key point detection】人脸关键点检测实现https://zhuanlan.zhihu.com/p/52525598
OpenCV实战：人脸关键点检测（FaceMark）https://zhuanlan.zhihu.com/p/35390012
CenterFace+TensorRT部署人脸和关键点检测400fpshttps://zhuanlan.zhihu.com/p/106774468
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#图像超分辨率
深度学习图像超分辨率综述https://zhuanlan.zhihu.com/p/57564211
从SRCNN到EDSR，总结深度学习端到端超分辨率方法发展历程https://zhuanlan.zhihu.com/p/31664818
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#行人重识别
【CVPR2019正式公布】行人重识别论文https://zhuanlan.zhihu.com/p/62843442
【CVPR2019正式公布】行人重识别论文https://zhuanlan.zhihu.com/p/62843442
2019 行人再识别年度进展回顾https://zhuanlan.zhihu.com/p/64004977
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#图像着色
Awesome-Image-Colorizationhttps://github.com/MarkMoHR/Awesome-Image-Colorization
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#边检测
Awesome-Edge-Detection-Papershttps://github.com/MarkMoHR/Awesome-Edge-Detection-Papers
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#ocr文本检测
2019CVPR文本检测综述https://zhuanlan.zhihu.com/p/67319122
OCR文字处理https://zhuanlan.zhihu.com/p/65707543
自然场景文本检测识别技术综述https://mp.weixin.qq.com/s?__biz=MzU4MjQ3MDkwNA==&mid=2247485142&idx=1&sn=c0e01da30eb5e750be453eabe4be2bf4&chksm=fdb69b41cac11257ae22c7dac395e9651dab628fc35dd6d3c02d9566a8c7f5f2b56353d58a64&token=1065243837&lang=zh_CN#rd
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#点云
awesome-point-cloud-analysishttps://zhuanlan.zhihu.com/p/65690433
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#细粒度图像分类
超全深度学习细粒度图像分析：项目、综述、教程一网打尽https://zhuanlan.zhihu.com/p/73542103
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#图像检索
上https://mp.weixin.qq.com/s/sM78DCOK3fuG2JrP2QaSZA
下https://mp.weixin.qq.com/s/yzVMDEpwbXVS0y-CwWSBEA
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#人群计数
人群计数http://chuansong.me/n/443237851736
1https://www.cnblogs.com/wmr95/p/8134692.html
2https://blog.csdn.net/u011285477/article/details/51954989
3https://blog.csdn.net/qingqingdeaini/article/details/79922549
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#教程
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#前馈神经网络
从基本原理到梯度下降，小白都能看懂的神经网络教程https://zhuanlan.zhihu.com/p/59385110
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#激活函数
激活函数一览https://zhuanlan.zhihu.com/p/30567264
深度学习中几种常见的激活函数理解与总结https://www.cnblogs.com/XDU-Lakers/p/10557496.html
一个激活函数需要具有哪些必要的属性https://www.zhihu.com/question/67366051
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#反向传播算法
反向传播算法（过程及公式推导）https://blog.csdn.net/u014313009/article/details/51039334
通俗理解神经网络BP传播算法https://zhuanlan.zhihu.com/p/24801814
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#优化问题
神经网络训练中的梯度消失与梯度爆炸https://zhuanlan.zhihu.com/p/25631496
梯度消失和梯度爆炸问题详解https://www.jianshu.com/p/3f35e555d5ba
详解深度学习中的梯度消失、爆炸原因及其解决方法https://zhuanlan.zhihu.com/p/33006526
神经网络梯度消失和梯度爆炸及解决办法https://blog.csdn.net/program_developer/article/details/80032376
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#卷积层
A Comprehensive Introduction to Different Types of Convolutions in Deep Learninghttps://towardsdatascience.com/a-comprehensive-introduction-to-different-types-of-convolutions-in-deep-learning-669281e58215
上https://www.leiphone.com/news/201902/D2Mkv61w9IPq9qGh.html
下https://www.leiphone.com/news/201902/biIqSBpehsaXFwpN.html?uniqueCode=OTEsp9649VqJfUcO
卷积有多少种？一文读懂深度学习中的各种卷积https://zhuanlan.zhihu.com/p/57575810
各种卷积https://www.cnblogs.com/cvtoEyes/p/8848815.html
Convolution Network及其变种（反卷积、扩展卷积、因果卷积、图卷积）https://www.cnblogs.com/yangperasd/p/7071657.html
深度学习基础--卷积类型https://zhuanlan.zhihu.com/p/59839551
变形卷积核、可分离卷积https://zhuanlan.zhihu.com/p/28749411
对深度可分离卷积、分组卷积、扩张卷积、转置卷积（反卷积）的理解https://blog.csdn.net/chaolei3/article/details/79374563
反卷积https://buptldy.github.io/2016/10/29/2016-10-29-deconv/
Dilated/Atrous conv 空洞卷积/多孔卷积https://blog.csdn.net/silence2015/article/details/79748729
卷积层输出大小尺寸计算及 “SAME” 和 “VALID”https://blog.csdn.net/weixin_37697191/article/details/89527315
卷积的三种模式full, same, valid以及padding的same, validhttps://zhuanlan.zhihu.com/p/62760780
正常卷积与空洞卷积输出特征图与感受野大小的计算https://blog.csdn.net/qq_43232545/article/details/103317773
【Tensorflow】tf.nn.depthwise_conv2d如何实现深度卷积?https://blog.csdn.net/mao_xiao_feng/article/details/78003476
【Tensorflow】tf.nn.atrous_conv2d如何实现空洞卷积？https://blog.csdn.net/mao_xiao_feng/article/details/78003730
【Tensorflow】tf.nn.separable_conv2d如何实现深度可分卷积?https://blog.csdn.net/mao_xiao_feng/article/details/78002811
【TensorFlow】tf.nn.conv2d_transpose是怎样实现反卷积的？https://blog.csdn.net/mao_xiao_feng/article/details/71713358
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#池化层
卷积神经网络中的各种池化操作https://zhuanlan.zhihu.com/p/112216409
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#卷积神经网络
卷积神经网络工作原理https://www.zhihu.com/question/39022858
「七夕的礼物」: 一日搞懂卷积神经网络https://zhuanlan.zhihu.com/p/28863709
一文读懂卷积神经网络中的1x1卷积核https://zhuanlan.zhihu.com/p/40050371
如何理解神经网络中通过add和concate的方式融合特征？https://blog.csdn.net/xiaojiajia007/article/details/86008415
神经网络中对需要concat的特征进行线性变换然后相加是否好于直接concat?https://www.zhihu.com/question/389912594/answer/1178054600
CNN 模型所需的计算力（flops）和参数（parameters）数量是怎么计算的？https://www.zhihu.com/question/65305385
深度学习中卷积的参数量和计算量https://www.cnblogs.com/hejunlin1992/p/12978988.html
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#图像分类网络详解
经典CNN模型LeNet解读https://zhuanlan.zhihu.com/p/41736894
机器学习进阶笔记之三 | 深入理解Alexnethttps://zhuanlan.zhihu.com/p/22659166
一文读懂VGG网络https://zhuanlan.zhihu.com/p/41423739
Inception V1,V2,V3,V4 模型总结https://zhuanlan.zhihu.com/p/52802896
ResNet解析https://blog.csdn.net/lanran2/article/details/79057994
一文简述ResNet及其多种变体https://zhuanlan.zhihu.com/p/35985680
CapsNet入门系列http://mp.weixin.qq.com/s?__biz=MzI3ODkxODU3Mg==&mid=2247484099&idx=1&sn=97e209f1a9860c8d8c51e81d98fc8a0a&chksm=eb4ee600dc396f16624a33cdfc0ead905e62ae9447b49b20146020e6cbd7d71f089101512a40&scene=21#wechat_redirect
CapsNet入门系列之一：胶囊网络背后的直觉http://mp.weixin.qq.com/s?__biz=MzI3ODkxODU3Mg==&mid=2247484099&idx=1&sn=97e209f1a9860c8d8c51e81d98fc8a0a&chksm=eb4ee600dc396f16624a33cdfc0ead905e62ae9447b49b20146020e6cbd7d71f089101512a40&scene=21#wechat_redirect
CapsNet入门系列之二：胶囊如何工作http://mp.weixin.qq.com/s?__biz=MzI3ODkxODU3Mg==&mid=2247484165&idx=1&sn=0ca679e3a5f499f8d8addb405fe3df83&chksm=eb4ee7c6dc396ed0a330fcac12690110bcaf9a8a10794dbc5e1a326c69ecbb140140f55fd6ba&scene=21#wechat_redirect
CapsNet入门系列之三：囊间动态路由算法http://mp.weixin.qq.com/s?__biz=MzI3ODkxODU3Mg==&mid=2247484433&idx=1&sn=3afe4605bc2501eebbc41c6dd1af9572&chksm=eb4ee0d2dc3969c4619d6c1097d5c949c76c6c854e60d36eba4388da2c3855747818d062c90a&scene=21#wechat_redirect
CapsNet入门系列之四：胶囊网络架构https://mp.weixin.qq.com/s/6CRSen8P6zKaMGtX8IRfqw
深入剖析MobileNet和它的变种（例如：ShuffleNet）为什么会变快？https://zhuanlan.zhihu.com/p/158591662
CNN模型之ShuffleNethttps://zhuanlan.zhihu.com/p/32304419
ShuffleNet V2和四个网络架构设计准则https://zhuanlan.zhihu.com/p/40980942
ResNeXt 深入解读与模型实现https://zhuanlan.zhihu.com/p/78019001
如何评价Momenta ImageNet 2017夺冠架构SENet?https://www.zhihu.com/question/63460684
CBAM：卷积块注意力模块https://zhuanlan.zhihu.com/p/79419670
CBAM: Convolutional Block Attention Modulehttps://zhuanlan.zhihu.com/p/65529934
SKNet——SENet孪生兄弟篇https://zhuanlan.zhihu.com/p/59690223
GCNet：当Non-local遇见SENethttps://zhuanlan.zhihu.com/p/64988633
深度学习笔记（十一）网络 Inception, Xception, MobileNet, ShuffeNet, ResNeXt, SqueezeNet, EfficientNet, MixConvhttps://www.cnblogs.com/xuanyuyt/p/11329998.html
如何评价最新的Octave Convolution？https://www.zhihu.com/question/320462422
ResNeSt 之语义分割https://zhuanlan.zhihu.com/p/136105870
关于ResNeSt的点滴疑惑https://zhuanlan.zhihu.com/p/133805433
ResNeSt在刷榜之后被ECCV2020 strong rejecthttps://zhuanlan.zhihu.com/p/143214871
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#目标检测网络详解
目标检测的性能评价指标https://zhuanlan.zhihu.com/p/70306015
NMS和计算mAP时的置信度阈值和IoU阈值 https://zhuanlan.zhihu.com/p/75348108
白话mAPhttps://zhuanlan.zhihu.com/p/60834912
目标检测模型的评估指标mAP详解(附代码）https://zhuanlan.zhihu.com/p/37910324
深度学习中IU、IoU(Intersection over Union)https://blog.csdn.net/iamoldpan/article/details/78799857
Selective Search for Object Detection https://www.learnopencv.com/selective-search-for-object-detection-cpp-python/
（译文）https://blog.csdn.net/guoyunfei20/article/details/78723646
Region Proposal Network(RPN)https://zhuanlan.zhihu.com/p/106192020
边框回归(Bounding Box Regression)详解https://blog.csdn.net/zijin0802034/article/details/77685438
NMS——非极大值抑制https://blog.csdn.net/shuzfan/article/details/52711706
非极大值抑制NMS的python实现https://zhuanlan.zhihu.com/p/128125301
一文打尽目标检测NMS——精度提升篇https://zhuanlan.zhihu.com/p/151914931
一文打尽目标检测NMS——效率提升篇https://zhuanlan.zhihu.com/p/157900024
目标检测回归损失函数简介：SmoothL1/IoU/GIoU/DIoU/CIoU Losshttps://zhuanlan.zhihu.com/p/104236411
将CNN引入目标检测的开山之作：R-CNNhttps://zhuanlan.zhihu.com/p/23006190
R-CNN论文详解https://blog.csdn.net/u014696921/article/details/52824097
深度学习（十八）基于R-CNN的物体检测https://blog.csdn.net/hjimce/article/details/50187029
Fast R-CNNhttps://zhuanlan.zhihu.com/p/24780395
深度学习（六十四）Faster R-CNN物体检测https://blog.csdn.net/hjimce/article/details/73382553
你真的学会RoI Pooling了吗?https://zhuanlan.zhihu.com/p/59692298
目标检测论文阅读：Feature Pyramid Networks for Object Detectionhttps://zhuanlan.zhihu.com/p/36461718
SSDhttps://zhuanlan.zhihu.com/p/24954433
实例分割--Mask RCNN详解(ROI Align / Loss Function)https://www.codetd.com/article/2554465
令人拍案称奇的Mask RCNNhttps://zhuanlan.zhihu.com/p/37998710
何恺明大神的「Focal Loss」，如何更好地理解？https://zhuanlan.zhihu.com/p/32423092
FocalLoss 对样本不平衡的权重调节和减低损失值https://zhuanlan.zhihu.com/p/82148525
focal_loss 多类别和二分类 Pytorch代码实现https://blog.csdn.net/qq_33278884/article/details/91572173
多分类focal loss及其tensorflow实现https://blog.csdn.net/qq_39012149/article/details/96184383
堪比Focal Loss！解决目标检测中样本不平衡的无采样方法https://zhuanlan.zhihu.com/p/93658728
目标检测正负样本区分策略和平衡策略总结(一)https://zhuanlan.zhihu.com/p/138824387
目标检测正负样本区分策略和平衡策略总结(二)https://zhuanlan.zhihu.com/p/138828372
目标检测正负样本区分策略和平衡策略总结(三）https://zhuanlan.zhihu.com/p/144659734
YOLOhttp://www.mamicode.com/info-detail-2314392.html
目标检测|YOLO原理与实现https://zhuanlan.zhihu.com/p/32525231
图解YOLOhttps://zhuanlan.zhihu.com/p/24916786
【论文解读】Yolo三部曲解读——Yolov1https://zhuanlan.zhihu.com/p/70387154
目标检测|YOLOv2原理与实现(附YOLOv3)https://zhuanlan.zhihu.com/p/35325884?group_id=966229905398362112
YOLO2https://zhuanlan.zhihu.com/p/25167153
【论文解读】Yolo三部曲解读——Yolov2https://zhuanlan.zhihu.com/p/74540100
<机器爱学习>YOLO v3深入理解https://zhuanlan.zhihu.com/p/49556105
【论文解读】Yolo三部曲解读——Yolov3https://zhuanlan.zhihu.com/p/76802514
YOLOv4https://zhuanlan.zhihu.com/p/138510087
目标检测之CornerNethttps://arxiv.org/abs/1808.01244
1https://zhuanlan.zhihu.com/p/41825737
2https://blog.csdn.net/Hibercraft/article/details/81637451
3https://zhuanlan.zhihu.com/p/41759548
目标检测小tricks--样本不均衡处理https://zhuanlan.zhihu.com/p/60612064
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#图像分割网络详解
超像素、语义分割、实例分割、全景分割 傻傻分不清 https://zhuanlan.zhihu.com/p/50996404
语义分割、实例分割和全景分割的区别https://blog.csdn.net/u013066730/article/details/103613154
语义分割卷积神经网络快速入门https://blog.csdn.net/qq_20084101/article/details/80455877
图像语义分割入门+FCN/U-Net网络解析https://zhuanlan.zhihu.com/p/31428783
深入理解深度学习分割网络Ｕnethttps://blog.csdn.net/Formlsl/article/details/80373200
Unet神经网络为什么会在医学图像分割表现好？https://www.zhihu.com/question/269914775
图像语义分割的工作原理和CNN架构变迁https://zhuanlan.zhihu.com/p/38033032
语义分割中的Attention和低秩重建https://zhuanlan.zhihu.com/p/77834369
打通多个视觉任务的全能Backbone:HRNethttps://zhuanlan.zhihu.com/p/134253318
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#注意力机制
深度学习中的注意力模型（2017版）https://zhuanlan.zhihu.com/p/37601161
Attention Model（mechanism） 的 套路https://blog.csdn.net/bvl10101111/article/details/78470716
计算机视觉中的注意力机制（推荐）https://zhuanlan.zhihu.com/p/146130215
More About Attention（推荐）https://zhuanlan.zhihu.com/p/106662375
计算机视觉中的注意力机制https://zhuanlan.zhihu.com/p/32928645
NLP中的Attention Mechanismhttps://zhuanlan.zhihu.com/p/31547842
Transformer中的Attentionhttps://mp.weixin.qq.com/s/k8PdZAld2ANVoekuyQxI3w
综述：图像处理中的注意力机制https://bbs.cvmart.net/topics/2581
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#特征融合
盘点目标检测中的特征融合技巧（根据YOLO v4总结）https://zhuanlan.zhihu.com/p/141685352
多尺度融合介绍https://zhuanlan.zhihu.com/p/147820687
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#action
PyTorch官方实现ResNethttps://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
pytorch_resnet_cifar10https://github.com/akamaster/pytorch_resnet_cifar10
PyTorch 63.Coding for FLOPs, Params and Latencyhttps://zhuanlan.zhihu.com/p/268816646
先读懂CapsNet架构然后用TensorFlow实现https://zhuanlan.zhihu.com/p/30753326
目标检测-20种模型的原味代码汇总https://zhuanlan.zhihu.com/p/37056927
TensorFlow Object Detection API 教程https://blog.csdn.net/qq_36148847/article/details/79306762
TensorFlow 对象检测 API 教程1https://blog.csdn.net/qq_36148847/article/details/79306762
TensorFlow 对象检测 API 教程2https://blog.csdn.net/qq_36148847/article/details/79307598
TensorFlow 对象检测 API 教程3https://blog.csdn.net/qq_36148847/article/details/79307751
TensorFlow 对象检测 API 教程 4https://blog.csdn.net/qq_36148847/article/details/79307931
TensorFlow 对象检测 API 教程5https://blog.csdn.net/qq_36148847/article/details/79307933
在TensorFlow+Keras环境下使用RoI池化一步步实现注意力机制https://zhuanlan.zhihu.com/p/65327747
mxnet如何查看参数数量https://discuss.gluon.ai/t/topic/7216
mxnet查看FLOPShttps://github.com/likelyzhao/CalFLOPS-Mxnet
Pytorch-UNethttps://github.com/milesial/Pytorch-UNet
segmentation_models.pytorchhttps://github.com/qubvel/segmentation_models.pytorch
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#gan
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#发展史-1
千奇百怪的GAN变体https://zhuanlan.zhihu.com/p/26491601
苏剑林博客，讲解得淋漓尽致https://kexue.fm/tag/GAN/
The GAN Landscape：Losses, Architectures, Regularization, and Normalizationhttps://arxiv.org/pdf/1807.04720.pdf
深度学习新星：GAN的基本原理、应用和走向https://www.leiphone.com/news/201701/Kq6FvnjgbKK8Lh8N.html
GAN生成图像综述https://zhuanlan.zhihu.com/p/62746494
2017年GAN 计算机视觉相关paper汇总https://zhuanlan.zhihu.com/p/29882709
必读的10篇关于GAN的论文https://zhuanlan.zhihu.com/p/72745900
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#教程-1
GAN原理学习笔记https://zhuanlan.zhihu.com/p/27295635
GAN万字长文综述https://zhuanlan.zhihu.com/p/58812258
极端图像压缩的对抗生成网络https://zhuanlan.zhihu.com/p/35783437?group_id=969598777652420608
台湾大学李宏毅GAN教程https://www.youtube.com/watch?v=0CKeqXl5IY0&feature=youtu.be
Basichttps://github.com/Mikoto10032/DeepLearning/blob/master/books/GAN-Basic%20Idea%20(2017.04.21).pdf
Improvinghttps://github.com/Mikoto10032/DeepLearning/blob/master/books/GAN-Improving%20GAN%20(2017.05.05).pdf
CycleGAN：图片风格，想换就换 | ICCV 2017论文解读https://zhuanlan.zhihu.com/p/34711316
Wasserstein GANhttps://zhuanlan.zhihu.com/p/25071913
GAN：两者分布不重合JS散度为log2的数学证明https://blog.csdn.net/Invokar/article/details/88917214
用变分推断统一理解生成模型（VAE、GAN、AAE、ALI）https://zhuanlan.zhihu.com/p/40105143
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#action-1
GAN学习指南：从原理入门到制作生成Demohttps://zhuanlan.zhihu.com/p/24767059
机器之心GitHub项目：GAN完整理论推导与实现https://zhuanlan.zhihu.com/p/29837245
在Keras上实现GAN：构建消除图片模糊的应用https://zhuanlan.zhihu.com/p/35030377
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#rnn
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#发展史-2
从90年代的SRNN开始，纵览循环神经网络27年的研究进展https://zhuanlan.zhihu.com/p/32668465
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#教程-2
Awesome-Chinese-NLPhttps://github.com/crownpku/Awesome-Chinese-NLP
nlp-pytorch-zhhttps://github.com/apachecn/nlp-pytorch-zh
完全图解RNN、RNN变体、Seq2Seq、Attention机制https://zhuanlan.zhihu.com/p/28054589
循环神经网络(RNN, Recurrent Neural Networks)介绍https://blog.csdn.net/heyongluoyao8/article/details/48636251
RNN以及LSTM的介绍和公式梳理https://blog.csdn.net/Dark_Scope/article/details/47056361
（译）理解长短期记忆(LSTM) 神经网络https://zhuanlan.zhihu.com/p/24018768
一文读懂LSTM和RNNhttps://zhuanlan.zhihu.com/p/35878575?group_id=970350175025385472
探索LSTM：基本概念到内部结构https://zhuanlan.zhihu.com/p/27345523
翻译：深入理解LSTM系列https://blog.csdn.net/matrix_space/article/details/53374040
深入理解 LSTM 网络 (一)https://blog.csdn.net/matrix_space/article/details/53374040
深入理解 LSTM 网络 (二)https://blog.csdn.net/matrix_space/article/details/53376870
LSTMhttps://zhuanlan.zhihu.com/p/32085405
深度学习其五 循环神经网络https://zybuluo.com/hanbingtao/note/541458
用循环神经网络进行文件无损压缩：斯坦福大学提出DeepZiphttps://zhuanlan.zhihu.com/p/32582764
吴恩达序列建模课程https://patch-diff.githubusercontent.com/UHT2020/DeepLearning/blob/master
Coursera吴恩达《序列模型》课程笔记（1）-- 循环神经网络（RNN）https://zhuanlan.zhihu.com/p/34309635
Coursera吴恩达《序列模型》课程笔记（2）-- NLP & Word Embeddingshttps://zhuanlan.zhihu.com/p/34975871
Coursera吴恩达《序列模型》课程笔记（3）-- Sequence models & Attention mechanismhttps://zhuanlan.zhihu.com/p/35532553
NLP 秒懂词向量Word2vec的本质https://zhuanlan.zhihu.com/p/26306795
一篇通俗易懂的word2vechttps://zhuanlan.zhihu.com/p/35500923
YJango的Word Embedding--介绍https://zhuanlan.zhihu.com/p/27830489
nlp中的词向量对比：word2vec/glove/fastText/elmo/GPT/berthttps://zhuanlan.zhihu.com/p/56382372
词嵌入（word2vec）https://zh.diveintodeeplearning.org/chapter_natural-language-processing/word2vec.html
谈谈谷歌word2vec的原理https://blog.csdn.net/wangyangzhizhou/article/details/77073023
Word2Vec中为什么使用负采样？https://zhuanlan.zhihu.com/p/67117737
练习-word2vechttps://zhuanlan.zhihu.com/p/29200034
word2vec方法的实现和应用https://zhuanlan.zhihu.com/p/31886824
自然语言处理入门 word2vec 使用tensorflow自己训练词向量https://blog.csdn.net/wzdjsgf/article/details/79541492
使用tensorflow实现word2vec中文词向量的训练https://zhuanlan.zhihu.com/p/28979653
如何用TensorFlow训练词向量https://blog.csdn.net/wangyangzhizhou/article/details/77530479?locationNum=1&fps=1
聊聊 Transformerhttps://zhuanlan.zhihu.com/p/47812375
基于Transform的机器翻译系统https://zhuanlan.zhihu.com/p/144825330
基于word2vec训练词向量(一)https://zhuanlan.zhihu.com/p/35648927
基于word2vec训练词向量(二)https://zhuanlan.zhihu.com/p/35889385
自然语言处理中的自注意力机制（Self-Attention Mechanism）https://zhuanlan.zhihu.com/p/35041012
自然语言处理中注意力机制综述https://zhuanlan.zhihu.com/p/54491016
YJango的Word Embedding--介绍https://zhuanlan.zhihu.com/p/27830489
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#action-2
推荐：nlp-tutorialhttps://github.com/graykode/nlp-tutorial
nlp-tutorialhttps://github.com/lyeoni/nlp-tutorial
tensorflow中RNNcell源码分析以及自定义RNNCell的方法https://blog.csdn.net/liuchonge/article/details/78405185?locationNum=8&fps=1
TensorFlow中RNN实现的正确打开方式https://zhuanlan.zhihu.com/p/28196873
TensorFlow RNN 代码https://zhuanlan.zhihu.com/p/27906426
Tensorflow实现的深度NLP模型集锦https://zhuanlan.zhihu.com/p/67031035
用tensorflow LSTM如何预测股票价格https://zhuanlan.zhihu.com/p/33186759
TensorFlow的多层LSTM实践https://zhuanlan.zhihu.com/p/29797089
《安娜卡列尼娜》文本生成——利用TensorFlow构建LSTM模型https://zhuanlan.zhihu.com/p/27087310
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#gnn
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#发展史-3
Graph Neural Network（GNN）综述https://zhuanlan.zhihu.com/p/65539782
深度学习时代的图模型，清华发文综述图网络https://mp.weixin.qq.com/s?__biz=MzA3MzI4MjgzMw==&mid=2650754422&idx=4&sn=0dc881487f362322a875b4ce06e645f7&chksm=871a8908b06d001ef7386ccc752827c20711877a4a23d6a8318978095dd241d118257c607b22&scene=21#wechat_redirect
清华大学图神经网络综述：模型与应用https://mp.weixin.qq.com/s?__biz=MzA3MzI4MjgzMw==&mid=2650754558&idx=2&sn=7d79191b9ed30679d5d40e22d9cabdf8&chksm=871a8980b06d00962e0dbe984e1d3469214db31cb402b4725a0dfe330249a830b45cb26932b5&scene=21#wechat_redirect
图神经网络概述第三弹：来自IEEE Fellow的GNN综述https://zhuanlan.zhihu.com/p/54241746
GNN最全文献资料整理https://github.com/DeepGraphLearning/LiteratureDL4Graph
Awesome-Graph-Neural-Networkshttps://github.com/nnzhan/Awesome-Graph-Neural-Networks
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#教程-3
如何理解 Graph Convolutional Network（GCN）https://www.zhihu.com/question/54504471
图卷积网络(GCN)新手村完全指南https://zhuanlan.zhihu.com/p/54505069
何时能懂你的心——图卷积神经网络（GCN）https://zhuanlan.zhihu.com/p/71200936
图卷积网络GCN的理解与介绍https://zhuanlan.zhihu.com/p/90470499
一文读懂图卷积GCNhttps://zhuanlan.zhihu.com/p/89503068
2020 年 GNN 开卷有益与再谈图卷积https://zhuanlan.zhihu.com/p/101310106
【GCN】万字长文带你入门 GCNhttps://zhuanlan.zhihu.com/p/120311352
如何解决图神经网络（GNN）训练中过度平滑的问题？https://www.zhihu.com/question/346942899/answer/848298494
全连接的图卷积网络(GCN)和self-attention这些机制有什么区别联系https://www.zhihu.com/question/366088445
CNN与GCN的区别、联系及融合https://zhuanlan.zhihu.com/p/147654689
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#action-3
图卷积网络到底怎么做，这是一份极简的Numpy实现https://zhuanlan.zhihu.com/p/57235377
DGLhttps://docs.dgl.ai/index.html
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#三-深度模型的优化与正则化
1. 优化算法纵览http://fa.bianp.net/teaching/2018/eecs227at/
2. 从梯度下降到Adamhttps://zhuanlan.zhihu.com/p/27449596
3. 从梯度下降到拟牛顿法：盘点训练神经网络的五大学习算法https://zhuanlan.zhihu.com/p/25703402
4. 正则化技术总结https://zhuanlan.zhihu.com/p/35429054?group_id=966442942538444800
史上最全面的正则化技术总结与分析--part1https://zhuanlan.zhihu.com/p/35429054?group_id=966442942538444800
史上最全面的正则化技术总结与分析--part2https://zhuanlan.zhihu.com/p/35432128?group_id=966443101011738624
权重衰减（weight decay）与学习率衰减（learning rate decay）https://zhuanlan.zhihu.com/p/38709373
pytorch必须掌握的的4种学习率衰减策略https://zhuanlan.zhihu.com/p/93624972
5. 最优化算法系列（math）https://blog.csdn.net/chunyun0716/article/category/6188191/2
6. 神经网络训练中的梯度消失与梯度爆炸https://zhuanlan.zhihu.com/p/25631496
7. 神经网络的优化及训练https://zhuanlan.zhihu.com/p/36050743
8. 通俗讲解查全率和查准率https://zhuanlan.zhihu.com/p/35888543
全面梳理：准确率,精确率,召回率,查准率,查全率,假阳性,真阳性,PRC,ROC,AUC,F1https://zhuanlan.zhihu.com/p/34079183
机器学习之类别不平衡问题 (1) —— 各种评估指标https://zhuanlan.zhihu.com/p/34473430
机器学习之类别不平衡问题 (2) —— ROC和PR曲线https://zhuanlan.zhihu.com/p/34655990
AUC详解与python实现https://zhuanlan.zhihu.com/p/84035782
微平均和宏平均https://zhuanlan.zhihu.com/p/78628437
机器学习中的性能度量https://zhuanlan.zhihu.com/p/74980268
精确率、召回率、F1 值、ROC、AUC 各自的优缺点是什么https://www.zhihu.com/question/30643044
激活函数一览https://zhuanlan.zhihu.com/p/30567264
深度学习中几种常见的激活函数理解与总结https://www.cnblogs.com/XDU-Lakers/p/10557496.html
深度学习笔记(三)：激活函数和损失函数https://blog.csdn.net/u014595019/article/details/52562159
激活函数/损失函数汇总https://zhuanlan.zhihu.com/p/30385380
机器学习中常见的损失函数及其应用场景https://blog.csdn.net/zuolixiangfisher/article/details/88649110
PyTorch的十八个损失函数https://zhuanlan.zhihu.com/p/61379965
深度度量学习中的损失函数https://zhuanlan.zhihu.com/p/82199561
反向传播算法（过程及公式推导）https://blog.csdn.net/u014313009/article/details/51039334
通俗理解神经网络BP传播算法https://zhuanlan.zhihu.com/p/24801814
10. Coursera吴恩达《优化深度神经网络》课程笔记（3）-- 超参数调试、Batch正则化和编程框架https://zhuanlan.zhihu.com/p/30922689
11. 机器学习各种熵https://zhuanlan.zhihu.com/p/35423404
12. 距离和相似性度量https://zhuanlan.zhihu.com/p/27305237
13. 机器学习里的黑色艺术：normalization, standardization, regularizationhttps://zhuanlan.zhihu.com/p/29974820
数据标准化/归一化normalizationhttps://blog.csdn.net/pipisorry/article/details/52247379
特征工程中的「归一化」有什么作用？https://www.zhihu.com/question/20455227
14. LSTM系列的梯度问题https://zhuanlan.zhihu.com/p/36101196
15. 损失函数整理https://zhuanlan.zhihu.com/p/35027284
16. 详解残差块为何有助于解决梯度弥散问题https://zhuanlan.zhihu.com/p/28124810
17. FAIR何恺明等人提出组归一化：替代批归一化，不受批量大小限制https://zhuanlan.zhihu.com/p/34858971
18. Batch Normalization（BN）https://patch-diff.githubusercontent.com/UHT2020/DeepLearning/blob/master
1 https://zhuanlan.zhihu.com/p/26702482
2 https://blog.csdn.net/hjimce/article/details/50866313
3 https://bbs.cvmart.net/topics/576
4 https://blog.csdn.net/edogawachia/article/details/80040456
5https://zhuanlan.zhihu.com/p/38176412
6https://www.zhihu.com/question/38102762
7https://zhuanlan.zhihu.com/p/52132614
19. 详解深度学习中的Normalization，不只是BNhttps://zhuanlan.zhihu.com/p/33173246
如何区分并记住常见的几种 Normalization 算法https://zhuanlan.zhihu.com/p/69659844
20. BFGShttps://blog.csdn.net/philosophyatmath/article/details/70173128
21. 详解深度学习中的梯度消失、爆炸原因及其解决方法https://zhuanlan.zhihu.com/p/33006526
神经网络梯度消失和梯度爆炸及解决办法https://blog.csdn.net/program_developer/article/details/80032376
22. Dropouthttps://arxiv.org/pdf/1207.0580.pdf
1https://blog.csdn.net/stdcoutzyx/article/details/49022443
2https://blog.csdn.net/hjimce/article/details/50413257
3https://blog.csdn.net/shuzfan/article/details/50580915
系列解读Dropouthttps://blog.csdn.net/shuzfan/article/details/50580915
23.谱归一化（Spectral Normalization）的理解https://blog.csdn.net/StreamRock/article/details/83590347
常见向量范数和矩阵范数https://blog.csdn.net/left_la/article/details/9159949
谱范数正则（Spectral Norm Regularization）的理解https://blog.csdn.net/StreamRock/article/details/83539937
24.L1正则化与L2正则化https://zhuanlan.zhihu.com/p/35356992
深入理解L1、L2正则化https://zhuanlan.zhihu.com/p/29360425
L2正则=Weight Decay？并不是这样https://zhuanlan.zhihu.com/p/40814046
都9102年了，别再用Adam + L2 regularizationhttps://zhuanlan.zhihu.com/p/63982470
25.为什么选用交叉熵而不是MSEhttps://zhuanlan.zhihu.com/p/61944055
为什么使用交叉熵作为损失函数https://zhuanlan.zhihu.com/p/63731947
二元分类为什么不能用MSE做为损失函数？http://sofasofa.io/forum_main_post.php?postid=1001792
为什么平方损失函数不适用分类问题？https://www.zhihu.com/question/319865092
浅谈神经网络中的梯度爆炸问题https://zhuanlan.zhihu.com/p/32154263
为什么weight decay能够防止过拟合https://www.zhihu.com/question/65626362
交叉熵代价函数（作用及公式推导）https://blog.csdn.net/u014313009/article/details/51043064
交叉熵损失的来源、说明、求导与pytorch实现https://zhuanlan.zhihu.com/p/67782576
Softmax函数与交叉熵https://zhuanlan.zhihu.com/p/27223959
极大似然估计与最小化交叉熵损失或者KL散度为什么等价https://zhuanlan.zhihu.com/p/84764177
梯度下降优化算法纵览http://ruder.io/optimizing-gradient-descent/
1https://blog.csdn.net/qq_23269761/article/details/80901411
2https://www.cnblogs.com/guoyaohua/p/8542554.html
几种优化算法的比较（BGD、SGD、Adam、RMSPROP）https://blog.csdn.net/qq_32172681/article/details/100979476
详解softmax函数以及相关求导过程https://zhuanlan.zhihu.com/p/25723112
softmax的log似然代价函数（公式求导）https://blog.csdn.net/u014313009/article/details/51045303
【技术综述】一文道尽softmax loss及其变种https://zhuanlan.zhihu.com/p/34044634
从最优化的角度看待Softmax损失函数https://zhuanlan.zhihu.com/p/45014864
Softmax理解之二分类与多分类https://zhuanlan.zhihu.com/p/45368976
Softmax理解之Smooth程度控制https://zhuanlan.zhihu.com/p/49939159
Softmax理解之marginhttps://zhuanlan.zhihu.com/p/52108088
神经网络中的权重初始化一览：从基础到Kaiminghttps://zhuanlan.zhihu.com/p/62850258
深度学习中常见的权重初始化方法https://zhuanlan.zhihu.com/p/138064188
深度学习中神经网络的几种权重初始化方法https://blog.csdn.net/u012328159/article/details/80025785
谈谈神经网络权重为什么不能初始化为0https://zhuanlan.zhihu.com/p/75879624
神经网络中的偏置（bias）究竟有这么用？https://www.zhihu.com/question/305340182
深度学习里面的偏置为什么不加正则？https://www.zhihu.com/question/66894061
为什么说bagging是减少variance，而boosting是减少bias?https://www.zhihu.com/question/26760839
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#四-炼丹术士那些事
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#调参经验
训练的神经网络不工作？一文带你跨过这37个坑https://blog.csdn.net/jiandanjinxin/article/details/77190687
Deep Learning 之 训练过程中出现NaN问题https://blog.csdn.net/BVL10101111/article/details/76086344
神经网络训练trickhttps://zhuanlan.zhihu.com/p/59918821
你有哪些deep learning（rnn、cnn）调参的经验？https://www.zhihu.com/question/41631631
GAN的一些小trickhttps://zhuanlan.zhihu.com/p/27725664
深度学习与计算机视觉系列(8)_神经网络训练与注意点https://blog.csdn.net/han_xiaoyang/article/details/50521064
神经网络训练loss不下降原因集合https://blog.csdn.net/liuweiyuxiang/article/details/80856991
loss不下降的解决方法https://blog.csdn.net/zongza/article/details/89185852
深度学习：欠拟合问题的几种解决方案https://blog.csdn.net/u014038273/article/details/84108688
过拟合和欠拟合问题https://blog.csdn.net/mzpmzk/article/details/79741682
机器学习：如何找到最优学习率https://blog.csdn.net/whut_ldz/article/details/78882871
实现https://github.com/L1aoXingyu/torchlib
神经网络中 warmup 策略为什么有效https://www.zhihu.com/question/338066667
不平衡数据集处理方法https://patch-diff.githubusercontent.com/UHT2020/DeepLearning/blob/master
其一https://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/
其二https://www.zhihu.com/question/285824343
其三https://blog.csdn.net/songhk0209/article/details/71484469
Awesome Imbalanced Learninghttps://github.com/ZhiningLiu1998/awesome-imbalanced-learning
Class-balanced-loss-pytorchhttps://github.com/vandit15/Class-balanced-loss-pytorch
同一个神经网络使用不同激活函数的表达能力是否一致https://www.zhihu.com/question/41841299
论文笔记之数据增广：mixuphttps://blog.csdn.net/ly244855983/article/details/78938667#%E8%AE%A8%E8%AE%BA
避坑指南：数据科学家新手常犯的13个错误https://zhuanlan.zhihu.com/p/44331706
凭什么相信CNN的结果?--可视化https://bindog.github.io/blog/2018/02/10/model-explanation/
凭什么相信你，我的CNN模型？（篇一：CAM和Grad-CAM)https://bindog.github.io/blog/2018/02/10/model-explanation/
pytorch-grad-camhttps://github.com/jacobgil/pytorch-grad-cam
Grad-CAM-tensorflowhttps://github.com/insikk/Grad-CAM-tensorflow
grad-cam.tensorflowhttps://github.com/Ankush96/grad-cam.tensorflow
cnn_visualizationhttps://github.com/js-fan/mxnet/tree/d2b802e2d2af3dae5b4ac941354602630d2ef1c7/example/cnn_visualization
凭什么相信你，我的CNN模型？（篇二：万金油LIME)http://bindog.github.io/blog/2018/02/11/model-explanation-2/
论文笔记:Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationhttps://www.jianshu.com/p/294ad9ae2e50
CV：基于Keras利用训练好的hdf5模型进行目标检测实现输出模型中的表情或性别的gradcam(可视化)https://blog.csdn.net/qq_41185868/article/details/80323646
大卷积核还是小卷积核?https://patch-diff.githubusercontent.com/UHT2020/DeepLearning/blob/master
1https://www.jianshu.com/p/d75375dd7ebd
2https://blog.csdn.net/kuangtun9713/article/details/79475457
模型可解释性差？你考虑了各种不确定性了吗？https://baijiahao.baidu.com/s?id=1608193373391996908
炼丹笔记系列https://patch-diff.githubusercontent.com/UHT2020/DeepLearning/blob/master
炼丹笔记一：样本不平衡问题https://zhuanlan.zhihu.com/p/56882616
炼丹笔记二：数据清洗https://zhuanlan.zhihu.com/p/56022212
炼丹笔记三：数据增强https://zhuanlan.zhihu.com/p/56139575
炼丹笔记四：小样本问题https://zhuanlan.zhihu.com/p/56365469
炼丹笔记五：数据标注https://zhuanlan.zhihu.com/p/56443169
炼丹笔记六 : 调参技巧https://zhuanlan.zhihu.com/p/56745640
炼丹笔记七：卷积神经网络模型设计https://zhuanlan.zhihu.com/p/57738934
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#刷排行榜的小技巧
Kaggle 六大比赛最全面解析（上）https://www.leiphone.com/news/201803/XBjvQriKTyTMPLcz.html
Kaggle 六大比赛最全面解析（下）https://www.leiphone.com/news/201803/chz1DNHqgVWNEm5t.html
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#图像分类-1
炼丹笔记三：数据增强https://zhuanlan.zhihu.com/p/56139575
数据增强(Data Augmentation)https://zhuanlan.zhihu.com/p/41679153
【技术综述】 深度学习中的数据增强（上）https://zhuanlan.zhihu.com/p/38345420
【技术综述】深度学习中的数据增强（下）https://zhuanlan.zhihu.com/p/38437739
深度学习数据增广技术一览https://zhuanlan.zhihu.com/p/144921458
《Bag of Tricks for Image Classification with CNN》https://zhuanlan.zhihu.com/p/53324148
pdfhttps://arxiv.org/pdf/1812.01187.pdf
深度神经网络模型训练中的最新tricks总结【原理与代码汇总】https://zhuanlan.zhihu.com/p/66080948
神经网络训练trickhttps://zhuanlan.zhihu.com/p/59918821
Kaggle解决方案分享https://patch-diff.githubusercontent.com/UHT2020/DeepLearning/blob/master
从0上手Kaggle图像分类挑战：冠军解决方案详解https://www.itcodemonkey.com/article/4898.html
Kaggle 冰山图像分类大赛近日落幕，看冠军团队方案有何亮点https://www.leiphone.com/news/201803/u40cjEZWArBfFaBm.html
【Kaggle冠军分享】图像识别和分类竞赛，数据增强及优化算法https://mp.weixin.qq.com/s/_S8EBBJ-u9g_fHp7I3ChMQ
识别座头鲸，Kaggle竞赛第一名解决方案解读https://zhuanlan.zhihu.com/p/58496385
kaggle 首战拿金牌总结https://zhuanlan.zhihu.com/p/60953933
16岁高中生夺冠Kaggle地标检索挑战赛！而且竟然是Kaggle老兵https://zhuanlan.zhihu.com/p/37522227
6次Kaggle计算机视觉类比赛赛后感https://zhuanlan.zhihu.com/p/37663895
Kaggle首战斩获第三-卫星图像识别https://zhuanlan.zhihu.com/p/63275166
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#目标检测-1
目标检测任务的优化策略trickshttps://zhuanlan.zhihu.com/p/56792817
目标检测小tricks--样本不均衡处理https://zhuanlan.zhihu.com/p/60612064
汇总|目标检测中的数据增强、backbone、head、neck、损失函数https://zhuanlan.zhihu.com/p/137769687
目标检测算法中的常见trickhttps://zhuanlan.zhihu.com/p/39262769
Bag of Freebies —— 提升目标检测模型性能的免费trickshttps://zhuanlan.zhihu.com/p/141878389
目标检测比赛中的tricks（已更新更多代码解析）https://zhuanlan.zhihu.com/p/102817180
Kaggle：肺癌自动诊断系统3D Deep Leaky Noisy-or Network 论文阅读https://www.jianshu.com/p/50158f8daf0d
干货|大神教你如何参加kaggle比赛——根据CT扫描图预测肺癌https://yq.aliyun.com/articles/89312
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#五-年度总结
新年大礼包：机器之心2018高分教程合集https://zhuanlan.zhihu.com/p/53717510
收藏、退出一气呵成，2019年机器之心干货教程都在这里了https://zhuanlan.zhihu.com/p/104022144
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#六-科研相关
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#深度学习框架
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#python3x先修
The Python Tutorialhttps://docs.python.org/3/tutorial/
廖雪峰Python教程https://www.liaoxuefeng.com/wiki/0014316089557264a6b348958f449949df42a6d3a2e542c000
菜鸟教程http://www.runoob.com/python3/python3-tutorial.html
给深度学习入门者的Python快速教程 - 基础篇https://zhuanlan.zhihu.com/p/24162430
Python - 100天从新手到大师https://github.com/jackfrued/Python-100-Days
Python中读取,显示,保存图片的方法https://blog.csdn.net/u010472607/article/details/78855816
Python的图像打开保存显示的几种方式https://blog.csdn.net/weixin_37619439/article/details/86559239
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#numpy先修
Quickstart tutorialhttps://www.numpy.org/devdocs/user/quickstart.html
Numpy快速入门(Numpy 1.14 官方文档中文翻译)https://www.jianshu.com/p/3e566f09a0cf
Numpy中文文档https://www.numpy.org.cn/index.html
给深度学习入门者的Python快速教程 - numpy和Matplotlib篇https://zhuanlan.zhihu.com/p/24309547
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#opencv-python
OpenCV-Python Tutorialshttps://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_tutorials.html
OpenCV官方教程中文版（For Python）https://www.cnblogs.com/Undo-self-blog/p/8423851.html
数字图像处理系列https://blog.csdn.net/feilong_csdn/article/category/8037591
python+OpenCV图像处理https://blog.csdn.net/qq_40962368/article/category/7688903
给深度学习入门者的Python快速教程 - 番外篇之Python-OpenCVhttps://zhuanlan.zhihu.com/p/24425116
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#pandas
Python 数据科学入门教程：Pandashttps://www.jianshu.com/p/d9774cf1fea5?utm_campaign=maleskine&utm_content=note&utm_medium=seo_notes&utm_source=recommendation
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#tensorflow
如何高效地学习 TensorFlow 代码https://www.zhihu.com/question/41667903
中文教程http://www.tensorfly.cn/tfdoc/tutorials/overview.html
TensorFlow官方文档https://www.w3cschool.cn/tensorflow_python/
CS20:Tensorflow for DeepLearning Researchhttp://web.stanford.edu/class/cs20si/syllabus.html
吴恩达TensorFlow专项课程https://zhuanlan.zhihu.com/p/62981537
【干货】史上最全的Tensorflow学习资源汇总https://zhuanlan.zhihu.com/p/35515805?group_id=967136289941897216
《21个项目玩转深度学习———基于TensorFlow的实践详解》https://github.com/hzy46/Deep-Learning-21-Examples
最全Tensorflow2.0 入门教程持续更新https://zhuanlan.zhihu.com/p/59507137
Github优秀开源教程https://github.com/search?o=desc&q=tensorflow+tutorial&s=&type=Repositories
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#mxnet
Gluonhttp://zh.gluon.ai/#
GluonCVhttps://gluon-cv.mxnet.io/index.html
GluonNLPhttp://gluon-nlp.mxnet.io/
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#pytorch
Pytorch版动手学深度学习https://github.com/ShusenTang/Dive-into-DL-PyTorch
PyTorch中文文档https://pytorch-cn.readthedocs.io/zh/latest/
WELCOME TO PYTORCH TUTORIALShttps://pytorch.org/tutorials/index.html
史上最全的PyTorch学习资源汇总https://zhuanlan.zhihu.com/p/64895011
【干货】史上最全的PyTorch学习资源汇总https://github.com/INTERMT/Awesome-PyTorch-Chinese
Hands-on tour to deep learning with PyTorchhttps://mlelarge.github.io/dataflowr-web/cea_edf_inria.html
pytorch学习(五)—图像的加载/读取方式https://www.jianshu.com/p/cfca9c4338e7
PyTorch—ImageFolder/自定义类 读取图片数据https://blog.csdn.net/wsp_1138886114/article/details/83620869
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#深度学习常用命令
command_for_deeplearninghttps://github.com/Stephenfang51/command_for_deeplearning/blob/master/command%20for%20deeplearning.md
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#python可视化
Top 50 matplotlib Visualizations – The Master Plots (with full python code)https://www.machinelearningplus.com/plots/top-50-matplotlib-visualizations-the-master-plots-python/
Python之MatPlotLib使用教程https://blog.csdn.net/zhw864680355/article/details/102500263
十分钟上手matplotlib，开启你的python可视化https://mp.weixin.qq.com/s/UfvEdzr-ZGmyT08yKDOchA
给深度学习入门者的Python快速教程 - numpy和Matplotlib篇https://zhuanlan.zhihu.com/p/24309547
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#标注工具
labelImghttps://github.com/tzutalin/labelImg
labelmehttps://github.com/wkentaro/labelme
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#数据集
1. 25个深度学习相关公开数据集https://zhuanlan.zhihu.com/p/35449783
2. 自然语言处理（NLP）数据集https://zhuanlan.zhihu.com/p/35423943
3.全唐诗(43030首)https://pan.baidu.com/s/1o7QlUhO
4. 伯克利大学公开数据集https://people.eecs.berkeley.edu/~taesung_park/
5. ACL 2018资源：100+ 预训练的中文词向量https://zhuanlan.zhihu.com/p/36835964
6. 预训练中文词向量https://github.com/Embedding/Chinese-Word-Vectors
7. 公开数据集种子库http://academictorrents.com
8. 计算机视觉，深度学习，数据挖掘数据集整理https://blog.csdn.net/c20081052/article/details/79814082
9. 计算机视觉著名数据集CV Datasetshttps://blog.csdn.net/accepthjp/article/details/51831026
10. 计算机视觉相关数据集和比赛https://blog.csdn.net/NNNNNNNNNNNNY/article/details/68485160
11. 这是一份非常全面的开源数据集，你，真的不想要吗？https://zhuanlan.zhihu.com/p/43846002
12. 人群密度估计现有主要数据集特点及其比较https://blog.csdn.net/weixin_40516558/article/details/81564464
13. DANBOORU2017: A LARGE-SCALE CROWDSOURCED AND TAGGED ANIME ILLUSTRATION DATASEThttps://www.gwern.net/Danbooru2017
14. 行人重识别数据集http://robustsystems.coe.neu.edu/sites/robustsystems.coe.neu.edu/files/systems/projectpages/reiddataset.html
15. 自然语言处理常见数据集、论文最全整理分享https://zhuanlan.zhihu.com/p/56144877
16. paper, code, sotahttps://paperswithcode.com/
17. 旷视RPC大型商品数据集发布！https://zhuanlan.zhihu.com/p/55627416
18. CVPR 2019「准满分」论文：英伟达推出首个跨摄像头汽车跟踪数据集(汽车Re-ID)https://zhuanlan.zhihu.com/p/60617001
19.【OCR技术】大批量生成文字训练集https://zhuanlan.zhihu.com/p/59052013
20. 语义分析数据集-MSRAhttps://github.com/msra-nlc/MSParS
IEEE DataPorthttps://ieee-dataport.org/
数据集市http://www.shujujishi.com/
医疗/医学图像数据集https://patch-diff.githubusercontent.com/UHT2020/DeepLearning/blob/master
Medical Data for Machine Learninghttps://github.com/beamandrew/medical-data
医疗领域图像挑战赛数据集https://grand-challenge.org/challenges/
【医学影像系列：一】数据集合集 最新最全https://blog.csdn.net/qq_31622015/article/details/90573874
medical-imaging-datasetshttps://github.com/sfikas/medical-imaging-datasets
【数据集】一文道尽医学图像数据集与竞赛https://zhuanlan.zhihu.com/p/50615907
医学图像数据集汇总https://zhuanlan.zhihu.com/p/102855802
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#记笔记工具
Markdown编辑器：Typora介绍https://zhuanlan.zhihu.com/p/67153848
Markdown语法介绍（常用）https://zhuanlan.zhihu.com/p/47897214
Markdown 语法手册 （完整整理版）https://blog.csdn.net/witnessai1/article/details/52551362
Markdown中Latex 数学公式基本语法https://blog.csdn.net/u014630987/article/details/70156489
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#会议期刊列表
国际会议日期表https://github.com/JackieTseng/conference_call_for_paper
ai-deadlineshttps://github.com/abhshkdz/ai-deadlines/
Keep Up With New Trendshttps://handong1587.github.io/deep_learning/2017/12/18/keep-up-with-new-trends.html
计算机会议排名等级https://blog.csdn.net/cserchen/article/details/40508181
中国计算机学会(CCF)推荐国际学术刊物和会议https://www.ccf.org.cn/Academic_Evaluation/By_category/
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#论文写作工具
Windows: Texlive+Texstudiohttps://jingyan.baidu.com/article/b2c186c83c9b40c46ff6ff4f.html
Ubuntu: Texlive+Texmakerhttps://jingyan.baidu.com/article/7c6fb4280b024180642c90e4.html
Latex：基本用法、表格、公式、算法https://blog.csdn.net/quiet_girl/article/details/72847208
LaTeX 各种命令，符号https://blog.csdn.net/garfielder007/article/details/51646604
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#论文画图工具
Visio2016https://msdn.itellyou.cn/
Matplotlibhttps://patch-diff.githubusercontent.com/UHT2020/DeepLearning#Python%E5%8F%AF%E8%A7%86%E5%8C%96
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#论文写作教程
刘知远_如何写一篇合格的NLP论文https://zhuanlan.zhihu.com/p/58752815
刘洋_如何写论文_V7http://nlp.csai.tsinghua.edu.cn/~ly/talks/cwmt14_tut.pdf
如何端到端地写科研论文-邱锡鹏https://xpqiu.github.io/slides/20181019-PaperWriting.pdf
论文Introduction写作其一https://zhuanlan.zhihu.com/p/33876355
论文Introduction写作其二https://zhuanlan.zhihu.com/p/52494933
论文Introduction写作其三https://zhuanlan.zhihu.com/p/52494879
毕业论文怎么写https://zhuanlan.zhihu.com/c_179195484
浅谈学术论文rebuttalhttps://zhuanlan.zhihu.com/p/104298923
学术论文投稿与返修（Rebuttal）分享https://zhuanlan.zhihu.com/p/344008879
研之成理写作实验室https://zhuanlan.zhihu.com/rationalscience-writing-lab
智源论坛·论文写作专题报告会https://patch-diff.githubusercontent.com/UHT2020/DeepLearning/blob/master
《论文写作小白的成长之路》https://zhuanlan.zhihu.com/p/135989892
《谈如何写一篇合格的国际学术论文》https://zhuanlan.zhihu.com/p/136005095
《计算机视觉会议论文从投稿到接收》https://zhuanlan.zhihu.com/p/139571199
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#researchgos
ResearchGo:研究生活第一帖——文献检索与管理https://zhuanlan.zhihu.com/p/22323250?refer=wjdml
ResearchGo:研究生活第二贴——文献阅读https://zhuanlan.zhihu.com/p/22402393?refer=wjdml
ResearchGo:研究生活第三帖——阅读辅助https://zhuanlan.zhihu.com/p/22622502?refer=wjdml
ResearchGo:研究生活第四帖——文献调研https://zhuanlan.zhihu.com/p/23178836?refer=wjdml
ResearchGo:研究生活第五帖——文献综述https://zhuanlan.zhihu.com/p/23356843?refer=wjdml
ResearchGo:研究生活第六帖——如何讲论文https://zhuanlan.zhihu.com/p/23872063?refer=wjdml
ResearchGo:研究生活第七帖——专利检索与申请https://zhuanlan.zhihu.com/p/25191025
ResearchGo:研究生活第八帖——写论文、做PPT、写文档必备工具集锦https://zhuanlan.zhihu.com/p/62100815
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#毕业论文排版
吐血推荐收藏的学位论文排版教程（完整版）https://zhuanlan.zhihu.com/p/52495345
论文怎么写——如何修改毕业论文格式https://zhuanlan.zhihu.com/p/35951260
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#信号处理
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#傅里叶变换
傅里叶分析之掐死教程（完整版）更新于2014.06.06https://zhuanlan.zhihu.com/p/19763358
如何简明的总结傅里叶变换？https://www.zhihu.com/question/34899574/answer/612923473
从连续时间傅里叶级数到快速傅里叶变换https://blog.csdn.net/clover13/article/details/79469851
十分简明易懂的FFT（快速傅里叶变换）https://blog.csdn.net/enjoy_pascal/article/details/81478582
傅里叶级数推导过程https://blog.csdn.net/hanxiaohu88/article/details/8245687
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#小波变换
形象易懂讲解算法I——小波变换https://zhuanlan.zhihu.com/p/22450818
小波变换完美通俗讲解系列之 （一）https://zhuanlan.zhihu.com/p/44215123
小波变换完美通俗讲解系列之 （二）https://zhuanlan.zhihu.com/p/44217268
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#实战
MWCNN中使用的haar小波变换 pytorchhttps://www.cnblogs.com/wanghui-garcia/p/12524515.html
【小波变换】小波变换入门----haar小波https://blog.csdn.net/baidu_27643275/article/details/84826773
（3）小波变换原理及应用https://blog.csdn.net/hhaowang/article/details/82909332
图像处理-小波变换https://blog.csdn.net/qq_30815237/article/details/89704855
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#机器学习理论与实战
机器学习原理https://github.com/shunliz/Machine-Learning
ID3、C4.5、CART、随机森林、bagging、boosting、Adaboost、GBDT、xgboost算法总结https://zhuanlan.zhihu.com/p/34534004
数据挖掘十大算法简要说明http://www.cnblogs.com/en-heng/p/5013995.html
机器学习十大经典算法入门https://blog.csdn.net/qq_42379006/article/details/80741808
【算法模型】轻松看懂机器学习十大常用算法https://www.cnblogs.com/ljt1412451704/p/9678248.html
AdaBoost到GBDT系列https://patch-diff.githubusercontent.com/UHT2020/DeepLearning/blob/master
当我们在谈论GBDT：从 AdaBoost 到 Gradient Boostinghttps://zhuanlan.zhihu.com/p/25096501?refer=data-miner
当我们在谈论GBDT：Gradient Boosting 用于分类与回归https://zhuanlan.zhihu.com/p/25257856?refer=data-miner
当我们在谈论GBDT：其他 Ensemble Learning 算法https://zhuanlan.zhihu.com/p/25443980
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#机器学习理论篇之经典算法
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#信息论
1. 机器学习中的各种熵https://zhuanlan.zhihu.com/p/35423404
2. 从香农熵到手推KL散度：纵览机器学习中的信息论https://zhuanlan.zhihu.com/p/32985487
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#多层感知机mlp
多层感知机（MLP）学习与总结博客https://blog.csdn.net/baidu_33718858/article/details/84972537
多层感知机：Multi-Layer Perceptronhttps://blog.csdn.net/xholes/article/details/78461164
神经网络基础-多层感知器(MLP)https://blog.csdn.net/weixin_38206214/article/details/81137911
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#k近邻knn
机器学习之KNN（k近邻）算法详解https://blog.csdn.net/sinat_30353259/article/details/80901746
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#k均值k-means
Kmeans聚类算法详解https://blog.csdn.net/qq_32892383/article/details/80107795
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#朴素贝叶斯naive-bayesian
一个例子搞清楚（先验分布/后验分布/似然估计）https://blog.csdn.net/qq_23947237/article/details/78265026
朴素贝叶斯分类器（Naive Bayesian Classifier）https://blog.csdn.net/qq_32690999/article/details/78737393
朴素贝叶斯分类器 详细解析https://blog.csdn.net/qq_17073497/article/details/81076250
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#决策树decision-tree
最常见核心的决策树算法详细介绍，含ID3、C4.5、CART:star:https://mp.weixin.qq.com/s/lXaPZyNrgG9LBv-JHdGm9A
最常用的决策树算法！Random Forest、Adaboost、GBDT 算法:star:https://mp.weixin.qq.com/s/Nl_-PdF0nHBq8yGp6AdI-Q
终于有人把XGBoost 和 LightGBM 讲明白了，项目中最主流的集成算法！:star:https://mp.weixin.qq.com/s/LoX987dypDg8jbeTJMpEPQ
为什么xgboost要用泰勒展开，优势在哪里http://blog.itblood.com/4082.html
Python3《机器学习实战》学习笔记（二）：决策树基础篇之让我们从相亲说起https://blog.csdn.net/c406495762/article/details/75663451
Python3《机器学习实战》学习笔记（三）：决策树实战篇之为自己配个隐形眼镜https://blog.csdn.net/c406495762/article/details/76262487
机器学习实战教程（十三）：树回归基础篇之CART算法与树剪枝http://cuijiahua.com/blog/2017/12/ml_13_regtree_1.html
《机器学习实战》基于信息论的三种决策树算法(ID3,C4.5,CART)https://blog.csdn.net/gamer_gyt/article/details/51242815
说说决策树剪枝算法https://zhuanlan.zhihu.com/p/31404571
机器学习实战 第九章 树回归https://blog.csdn.net/namelessml/article/details/52595066
决策树值ID3、C4.5实现https://blog.csdn.net/u014688145/article/details/53212112
决策树之CART实现https://blog.csdn.net/u014688145/article/details/53326910
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#随机森林random-forest
随机森林和GBDT的区别https://blog.csdn.net/login_sonata/article/details/73929426
随机森林（Random Forest）入门与实战https://blog.csdn.net/sb19931201/article/details/52601058
随机森林之特征选择https://www.cnblogs.com/justcxtoworld/p/3447231.html
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#线性回归linear-regression
线性回归最小二乘法和最大似然估计https://blog.csdn.net/lt793843439/article/details/91392646
【从入门到放弃】线性回归https://zhuanlan.zhihu.com/p/147297924
线性回归(频率学派-最大似然估计)与岭回归(贝叶斯角度-最大后验估计)的概率解释https://blog.csdn.net/z_feng12489/article/details/101388745
机器学习笔记四：线性回归回顾与logistic回归https://blog.csdn.net/xierhacker/article/details/53316138
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#逻辑回归logistic-regression
【机器学习面试总结】—— LR（逻辑回归）https://zhuanlan.zhihu.com/p/100763009
【机器学习面试题】逻辑回归篇https://zhuanlan.zhihu.com/p/62653034
极大似然概率和最小损失函数，以及正则化简介https://www.jianshu.com/p/9d2686cd407e
GLM(广义线性模型) 与 LR(逻辑回归) 详解https://blog.csdn.net/Cdd2xd/article/details/75635688
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#支持向量机svm
【机器学习面试总结】—— SVMhttps://zhuanlan.zhihu.com/p/93715996
SVM系列-从基础到掌握https://zhuanlan.zhihu.com/p/61123737
SVM通俗导论 Julyhttps://github.com/Mikoto10032/DeepLearning/blob/master/books/%E6%94%AF%E6%8C%81%E5%90%91%E9%87%8F%E6%9C%BA%E9%80%9A%E4%BF%97%E5%AF%BC%E8%AE%BA%EF%BC%88%E7%90%86%E8%A7%A3SVM%E7%9A%84%E4%B8%89%E5%B1%82%E5%A2%83%E7%95%8C%EF%BC%89LaTeX%E6%9C%80%E6%96%B0%E7%89%88_2015.1.9.pdf
核函数 https://patch-diff.githubusercontent.com/UHT2020/DeepLearning/blob/master
机器学习有很多关于核函数的说法，核函数的定义和作用是什么？https://www.zhihu.com/question/24627666
SVM中，高斯核为什么会把原始维度映射到无穷多维？https://www.zhihu.com/question/35602879
svm核函数的理解和选择https://blog.csdn.net/leonis_v/article/details/50688766
核函数和径向基核函数 (Radial Basis Function)--RBFhttps://blog.csdn.net/huang1024rui/article/details/51510611
SVM核函数https://blog.csdn.net/xiaowei_cqu/article/details/35993729
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#提升方法adaboost
当我们在谈论GBDT：从 AdaBoost 到 Gradient Boostinghttps://zhuanlan.zhihu.com/p/25096501
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#梯度提升决策树gbdt
LightGBM大战XGBoosthttps://zhuanlan.zhihu.com/p/35645973
概述XGBoost、Light GBM和CatBoost的同与不同https://zhuanlan.zhihu.com/p/34698733
XGBoost、LightGBM、Catboost总结https://www.cnblogs.com/lvdongjie/p/11391245.html
XGBoost、Light GBM和CatBoost的参数及性能比较https://zhuanlan.zhihu.com/p/34698733
梯度提升决策树https://zhuanlan.zhihu.com/p/36339161
GBDT原理及应用https://zhuanlan.zhihu.com/p/30339807
XGBOOST原理篇https://zhuanlan.zhihu.com/p/31654000
xgboost入门与实战（原理篇）https://blog.csdn.net/sb19931201/article/details/52557382
xgboost入门与实战（实战调参篇）https://blog.csdn.net/sb19931201/article/details/52577592
【干货合集】通俗理解kaggle比赛大杀器xgboosthttps://zhuanlan.zhihu.com/p/41417638
GBDT分类的原理及Python实现https://blog.csdn.net/bf02jgtrs00xktcx/article/details/82719765
GBDT原理及利用GBDT构造新的特征-Python实现https://blog.csdn.net/shine19930820/article/details/71713680
Python+GBDT算法实战——预测实现100%准确率https://www.jianshu.com/p/47e73a985ba1
xgboost之近似分位数算法（直方图算法）详解https://blog.csdn.net/m0_37870649/article/details/104561431
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#em期望最大化
人人都懂的EM算法 https://zhuanlan.zhihu.com/p/36331115
EM算法入门文章https://zhuanlan.zhihu.com/p/61768577
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#高斯混合模型gmm
高斯混合模型与EM算法的数学原理及应用实例https://zhuanlan.zhihu.com/p/67107370
高斯混合模型（GMM）https://zhuanlan.zhihu.com/p/30483076
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#马尔科夫决策过程mdp
马尔科夫决策过程之Markov Processes（马尔科夫过程）https://zhuanlan.zhihu.com/p/35124726
马尔科夫决策过程之Markov Reward Process（马尔科夫奖励过程）https://zhuanlan.zhihu.com/p/35231424
马尔科夫决策过程之Bellman Equation（贝尔曼方程）https://zhuanlan.zhihu.com/p/35261164
马尔科夫决策过程之Markov Decision Process(马尔科夫决策过程)https://zhuanlan.zhihu.com/p/35354956
马尔科夫决策过程之最优价值函数与最优策略https://zhuanlan.zhihu.com/p/35373905
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#条件随机场crf-判别式模型
如何轻松愉快地理解条件随机场https://zhuanlan.zhihu.com/p/104562658
如何用简单易懂的例子解释条件随机场（CRF）模型？它和HMM有什么区别？https://www.zhihu.com/question/35866596
HMM ,MHMM,CRF 优缺点与区别https://blog.csdn.net/u013378306/article/details/55213029
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#降维算法
数据降维算法-从PCA到LargeVishttps://zhuanlan.zhihu.com/p/62470700
12种降维方法终极指南（含Python代码）https://zhuanlan.zhihu.com/p/43225794
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#主成分分析pca
主成分分析（PCA）原理详解https://blog.csdn.net/program_developer/article/details/80632779
图文并茂的PCA教程https://blog.csdn.net/hustqb/article/details/78394058
PCA数学原理http://www.360doc.com/content/13/1124/02/9482_331688889.shtml
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#奇异值分解svd
强大的矩阵奇异值分解(SVD)及其应用https://www.cnblogs.com/LeftNotEasy/archive/2011/01/19/svd-and-applications.html
奇异值分解（SVD）https://zhuanlan.zhihu.com/p/29846048
奇异值分解(SVD)原理详解及推导https://blog.csdn.net/zhongkejingwang/article/details/43053513
SVD在推荐系统中的应用详解以及算法推导https://blog.csdn.net/zhongkejingwang/article/details/43083603
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#线性判别分析lda
教科书上的LDA为什么长这个样子？https://zhuanlan.zhihu.com/p/42238953
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#标签传播算法label-propagation-algorithm--
标签传播算法（Label Propagation）及Python实现https://blog.csdn.net/zouxy09/article/details/49105265
参考资料https://github.com/Mikoto10032/DeepLearning/blob/master/books/Semi-Supervised%20Learning%20with%20Graphs.pdf
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#蒙塔卡罗树搜索mcts
蒙特卡洛树搜索入门指南https://zhuanlan.zhihu.com/p/34950988
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#集成ensemble
集成学习之bagging,stacking,boosting概念理解https://zhuanlan.zhihu.com/p/41809927
Bagging和Boosting的总结https://www.zhihu.com/follow
集成学习法之bagging方法和boosting方法https://blog.csdn.net/qq_30189255/article/details/51532442
Bagging,Boosting,Stackinghttps://blog.csdn.net/Mr_tyting/article/details/72957853
常用的模型集成方法介绍：bagging、boosting 、stackinghttps://zhuanlan.zhihu.com/p/65888174
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#t分布随机邻居嵌入tsne
流形学习-高维数据的降维与可视化https://blog.csdn.net/u012162613/article/details/45920827
tSNEhttps://blog.csdn.net/flyingzhan/article/details/79521765
使用t-SNE可视化图像embeddinghttps://zhuanlan.zhihu.com/p/81400277
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#谱聚类spectral-clustering
谱聚类（Spectral Clustering）算法介绍https://blog.csdn.net/qq_24519677/article/details/82291867
聚类5--谱和谱聚类https://blog.csdn.net/xueyingxue001/article/details/51966980
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#异常点检测
数据挖掘中常见的「异常检测」算法有哪些？https://www.zhihu.com/question/280696035/answer/417091151
异常点检测算法综述https://zhuanlan.zhihu.com/p/30169110
异常检测的N种方法，其中有一个你一定想不到https://mp.weixin.qq.com/s/RYLlUJiYbWqGIhzflbRGEg
异常检测资源汇总：anomaly-detection-resourceshttps://zhuanlan.zhihu.com/p/158349346
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#机器学习实战篇
机器学习中，有哪些特征选择的工程方法？https://www.zhihu.com/question/28641663
机器学习（四）：数据预处理--特征工程概述https://zhuanlan.zhihu.com/p/103070096
特征工程完全手册 - 从预处理、构造、选择、降维、不平衡处理，到放弃https://zhuanlan.zhihu.com/p/94994902
特征工程中的「归一化」有什么作用https://www.zhihu.com/question/20455227
15分钟带你入门sklearn与机器学习——分类算法篇https://mp.weixin.qq.com/s?__biz=Mzg5NzAxMDgwNg==&mid=2247484110&idx=1&sn=b016e270d7b7707e6ad41a81ca45fc28&chksm=c0791fd7f70e96c103a8a2aebee166ce14f5648b3b889dd85dd9786f48b6b8269f11e5e27e1c&scene=21#wechat_redirect
如何为你的回归问题选择最合适的机器学习方法？https://zhuanlan.zhihu.com/p/62034592
十分钟上手sklearn：安装，获取数据，数据预处理https://zhuanlan.zhihu.com/p/105039597
十分钟上手sklearn：特征提取，常用模型，交叉验证https://zhuanlan.zhihu.com/p/105041301
MachineLearning_Pythonhttps://github.com/lawlite19/MachineLearning_Python
Machine Learning Course with Pythonhttps://github.com/machinelearningmindset/machine-learning-course
Statistical-Learning-Method_Codehttps://github.com/Dod-o/Statistical-Learning-Method_Code
Python3机器学习https://blog.csdn.net/c406495762/column/info/16415
含大牛总结的分类模型一般需要调节的参数https://www.jianshu.com/p/9d2452fc93c2
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#机器学习深度学习的一些研究方向
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#多任务学习multi-task-learning
模型汇总-14 多任务学习-Multitask Learning概述https://zhuanlan.zhihu.com/p/27421983
(译)深度神经网络的多任务学习概览(An Overview of Multi-task Learning in Deep Neural Networks)http://www.cnblogs.com/shuzirank/p/7141017.html
Multi-task Learning and Beyond: 过去，现在与未来https://zhuanlan.zhihu.com/p/138597214
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#零次学习zero-shot-learning
零次学习（Zero-Shot Learning）入门https://zhuanlan.zhihu.com/p/34656727
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#小样本学习few-shot-learning
few-shot learning是什么https://blog.csdn.net/xhw205/article/details/79491649
零次学习（Zero-Shot Learning）入门https://zhuanlan.zhihu.com/p/34656727
小样本学习（Few-shot Learning）综述https://zhuanlan.zhihu.com/p/61215293
Few-Shot Learning in CVPR 2019https://towardsdatascience.com/few-shot-learning-in-cvpr19-6c6892fc8c5
当小样本遇上机器学习 fewshot learninghttps://blog.csdn.net/mao_feng/article/details/78939864
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#多视觉学习multi-view-learning
Multi-view Learning 多视角学习入门https://blog.csdn.net/danliwoo/article/details/79278574
多视角学习 (Multi-View Learning)https://blog.csdn.net/shine19930820/article/details/77426599
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#嵌入embedding
万物皆Embedding，从经典的word2vec到深度学习基本操作item2vechttps://zhuanlan.zhihu.com/p/53194407
YJango的Word Embedding--介绍https://zhuanlan.zhihu.com/p/27830489
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#迁移学习transfer-learning
1. 迁移学习：经典算法解析https://blog.csdn.net/linolzhang/article/details/73358219
2. 什么是迁移学习 (Transfer Learning)？这个领域历史发展前景如何？https://www.zhihu.com/question/41979241
3. 迁移学习个人笔记https://github.com/Mikoto10032/DeepLearning/blob/master/notes/%E6%97%A5%E5%B8%B8%E9%98%85%E8%AF%BB%E7%AC%94%E8%AE%B0/2018_4_12_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0.pdf
迁移学习总结(One Shot Learning, Zero Shot Learning)https://blog.csdn.net/XJTU_NOC_Wei/article/details/77850221
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#域自适应domain-adaptation
Domain Adaptation视频教程（附PPT）及经典论文分享https://zhuanlan.zhihu.com/p/27519182
模型汇总15 领域适应性Domain Adaptation、One-shot/zero-shot Learning概述https://zhuanlan.zhihu.com/p/27449079
【深度学习】论文导读：无监督域适应（Deep Transfer Network: Unsupervised Domain Adaptation）https://blog.csdn.net/mao_xiao_feng/article/details/54426101
【论文阅读笔记】基于反向传播的无监督域自适应研究https://zhuanlan.zhihu.com/p/37298073
【Valse大会首发】领域自适应及其在人脸识别中的应用https://zhuanlan.zhihu.com/p/21441807
CVPR 2018：基于域适应弱监督学习的目标检测https://zhuanlan.zhihu.com/p/41126114
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#元学习meta-learning
OpenAI提出新型元学习方法EPG，调整损失函数实现新任务上的快速训练https://zhuanlan.zhihu.com/p/35869158?group_id=970310501209645056
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#强化学习reinforcement-learning
强化学习（Reinforcement Learning）知识整理https://zhuanlan.zhihu.com/p/25498081
强化学习从入门到放弃的资料https://zhuanlan.zhihu.com/p/34918639
强化学习入门https://zhuanlan.zhihu.com/p/25498081
强化学习入门 第一讲 MDPhttps://zhuanlan.zhihu.com/p/25498081
强化学习——从Q-Learning到DQN到底发生了什么？https://zhuanlan.zhihu.com/p/35882937
从强化学习到深度强化学习（上）https://zhuanlan.zhihu.com/p/35688924
从强化学习到深度强化学习（下）https://zhuanlan.zhihu.com/p/35965070
一文带你理解Q-Learning的搜索策略https://zhuanlan.zhihu.com/p/37048004
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#推荐系统recommendation-system
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#论文列表
Embedding从入门到专家必读的十篇论文https://zhuanlan.zhihu.com/p/58805184
Reco-papershttps://github.com/wzhe06/Reco-papers
Ad-papershttps://github.com/wzhe06/Ad-papers
deep-recommender-systemhttps://github.com/chocoluffy/deep-recommender-system
CTR预估系列入门手册https://zhuanlan.zhihu.com/p/243243145
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#教程-4
推荐系统从入门到接着入门https://zhuanlan.zhihu.com/p/27502172
深度学习推荐系统笔记https://zhuanlan.zhihu.com/p/133528693
推荐系统干货总结https://zhuanlan.zhihu.com/p/34004488
入门推荐系统，你不应该错过的知识清单https://zhuanlan.zhihu.com/p/54819505
从零开始了解推荐系统全貌https://zhuanlan.zhihu.com/p/259985388
推荐系统玩家 之 推荐系统入门——推荐系统的发展历程（上）https://zhuanlan.zhihu.com/p/148207613
推荐系统技术演进趋势：从召回到排序再到重排https://zhuanlan.zhihu.com/p/100019681
深入理解推荐系统：召回https://zhuanlan.zhihu.com/p/115690499
深入理解推荐系统：排序https://zhuanlan.zhihu.com/p/138235048
召回算法有哪些https://www.zhihu.com/question/423384620/answer/1687201890
《深度学习推荐系统》总结系列一https://zhuanlan.zhihu.com/p/138446984
《深度学习推荐系统》总结系列二https://zhuanlan.zhihu.com/p/140894123
推荐系统--完整的架构设计和算法(协同过滤、隐语义)https://zhuanlan.zhihu.com/p/81752025
从0到1打造推荐系统-架构篇https://zhuanlan.zhihu.com/p/123951784
协同过滤和基于内容推荐有什么区别？https://www.zhihu.com/question/19971859
CTR深度交叉特征入门总结https://zhuanlan.zhihu.com/p/257895631
推荐系统学习笔记https://blog.csdn.net/wuzhongqiang/category_10128687.html
https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#实战-1
AI-RecommenderSystemhttps://github.com/zhongqiangwu960812/AI-RecommenderSystem
team-learning-rshttps://github.com/datawhalechina/team-learning-rs
RecommendSystemPracticehttps://github.com/Magic-Bubble/RecommendSystemPractice
Surprisehttp://surpriselib.com/
Readme https://patch-diff.githubusercontent.com/UHT2020/DeepLearning#readme-ov-file
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