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https://patch-diff.githubusercontent.com/dade68/DeepLearning#deeplearning
https://patch-diff.githubusercontent.com/dade68/DeepLearning#入门资料
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
2. 《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
3. 《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
4. 《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
5. 《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
6. 《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
7. 《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
8. 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/special/opencourse/machinelearning.html
9. 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
10. 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
11. 深度学习 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
12. 台湾大学（NTU）李宏毅教授课程http://speech.ee.ntu.edu.tw/~tlkagk/index.html
13. 《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
14. 《强化学习》https://github.com/Mikoto10032/DeepLearning/blob/master/books/Reinforcement%20Learning.Sutton.pdf
15. hangdong的深度学习博客,论文推荐https://handong1587.github.io/categories.html#deep_learning-ref
16. CS20:Tensorflow for DeepLearning Researchhttp://web.stanford.edu/class/cs20si/syllabus.html
17. CS321-Hintonhttp://www.cs.toronto.edu/~tijmen/csc321/
18. 深度学习思维导图https://github.com/dformoso/deeplearning-mindmap
19. CS230: Deep Learninghttps://web.stanford.edu/class/cs230/
20. CS294-112http://rail.eecs.berkeley.edu/deeprlcourse/resources/
https://patch-diff.githubusercontent.com/dade68/DeepLearning#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
https://patch-diff.githubusercontent.com/dade68/DeepLearning#cnn
https://patch-diff.githubusercontent.com/dade68/DeepLearning#发展史
1. 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
2. 从LeNet-5到DenseNethttps://zhuanlan.zhihu.com/p/31006686
3. CNN图像分割简史：从R-CNN到Mask R-CNN（译）https://zhuanlan.zhihu.com/p/26652657
4. 深度学习之目标检测的前世今生（Mask R-CNN）https://zhuanlan.zhihu.com/p/32830206
5. 纵览轻量化卷积神经网络：SqueezeNet、MobileNet、ShuffleNet、Xceptionhttps://zhuanlan.zhihu.com/p/32746221
6. 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
7. 图像语义分割(Semantic segmentation) Surveyhttps://zhuanlan.zhihu.com/p/36801104
7. 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
8. 图像语义分割(Semantic segmentation) Surveyhttps://zhuanlan.zhihu.com/p/36801104
9. 语义分割 发展综述https://zhuanlan.zhihu.com/p/37618829
深度学习分类网络https://blog.csdn.net/PeaceInMind/article/details/78079263
卷积神经网络结构演变（form Hubel and Wiesel to SENet）https://zhuanlan.zhihu.com/p/34621135
从VGG到NASNet，一文概览图像分类网络https://zhuanlan.zhihu.com/p/35221368
From RCNN to YOLOhttps://patch-diff.githubusercontent.com/dade68/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-20种模型的原味代码汇总https://zhuanlan.zhihu.com/p/37056927
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
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
https://patch-diff.githubusercontent.com/dade68/DeepLearning#教程
卷积神经网络工作原理https://www.zhihu.com/question/39022858
变形卷积核、可分离卷积https://zhuanlan.zhihu.com/p/28749411
各种卷积https://www.cnblogs.com/cvtoEyes/p/8848815.html
反卷积https://buptldy.github.io/2016/10/29/2016-10-29-deconv/
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
Dilated/Atrous conv 空洞卷积/多孔卷积https://blog.csdn.net/silence2015/article/details/79748729
CNN模型之ShuffleNethttps://zhuanlan.zhihu.com/p/32304419
一文简述ResNet及其多种变体https://zhuanlan.zhihu.com/p/35985680
ResNet解析https://blog.csdn.net/lanran2/article/details/79057994
将CNN引入目标检测的开山之作：R-CNNhttps://zhuanlan.zhihu.com/p/23006190
深度学习（十八）基于R-CNN的物体检测https://blog.csdn.net/hjimce/article/details/50187029
R-CNN论文详解https://blog.csdn.net/u014696921/article/details/52824097
深度学习（六十四）Faster R-CNN物体检测https://blog.csdn.net/hjimce/article/details/73382553
先理解Mask R-CNN的工作原理，然后构建颜色填充器应用https://zhuanlan.zhihu.com/p/34816076
人脸检测和识别算法综述https://patch-diff.githubusercontent.com/dade68/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
语义分割卷积神经网络快速入门https://blog.csdn.net/qq_20084101/article/details/80455877
图像语义分割的工作原理和CNN架构变迁https://zhuanlan.zhihu.com/p/38033032
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
YOLOhttp://www.mamicode.com/info-detail-2314392.html
目标检测|YOLOv2原理与实现(附YOLOv3)https://zhuanlan.zhihu.com/p/35325884?group_id=966229905398362112
目标检测模型YOLO v3问世https://zhuanlan.zhihu.com/p/34995629
Attentionhttp://www.cnblogs.com/shouhuxianjian/p/7903097.html
1https://zhuanlan.zhihu.com/p/31547842
2https://blog.csdn.net/yideqianfenzhiyi/article/details/79422857
3https://blog.csdn.net/Wayne2019/article/details/78488142
4https://zhuanlan.zhihu.com/p/37601161
5https://blog.csdn.net/bvl10101111/article/details/78470716
一文读懂卷积神经网络中的1x1卷积核https://zhuanlan.zhihu.com/p/40050371
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
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
RelationNetworkhttps://www.zhihu.com/question/60784169
ShuffleNet V2和四个网络架构设计准则https://zhuanlan.zhihu.com/p/40980942
【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/dade68/DeepLearning#action
先读懂CapsNet架构然后用TensorFlow实现https://zhuanlan.zhihu.com/p/30753326
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
https://patch-diff.githubusercontent.com/dade68/DeepLearning#gan
https://patch-diff.githubusercontent.com/dade68/DeepLearning#发展史-1
千奇百怪的GAN变体https://zhuanlan.zhihu.com/p/26491601
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
https://patch-diff.githubusercontent.com/dade68/DeepLearning#教程-1
1. GAN原理学习笔记https://zhuanlan.zhihu.com/p/27295635
2. 极端图像压缩的对抗生成网络https://zhuanlan.zhihu.com/p/35783437?group_id=969598777652420608
3. 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
4. 2017年GAN 计算机视觉相关paper汇总https://zhuanlan.zhihu.com/p/29882709
5. 在Keras上实现GAN：构建消除图片模糊的应用https://zhuanlan.zhihu.com/p/35030377
6. CycleGAN：图片风格，想换就换 | ICCV 2017论文解读https://zhuanlan.zhihu.com/p/34711316
7. Wasserstein GANhttps://zhuanlan.zhihu.com/p/25071913
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/dade68/DeepLearning#action-1
1. GAN学习指南：从原理入门到制作生成Demohttps://zhuanlan.zhihu.com/p/24767059
2. 机器之心GitHub项目：GAN完整理论推导与实现https://zhuanlan.zhihu.com/p/29837245
https://patch-diff.githubusercontent.com/dade68/DeepLearning#rnn
https://patch-diff.githubusercontent.com/dade68/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/dade68/DeepLearning#教程-2
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
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/dade68/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
Word2Vechttps://patch-diff.githubusercontent.com/dade68/DeepLearning/blob/master
word2vec原理(一) CBOW与Skip-Gram模型基础https://www.cnblogs.com/pinard/p/7160330.html
word2vec原理(二) 基于Hierarchical Softmax的模型http://www.cnblogs.com/pinard/p/7243513.html
word2vec原理(三) 基于Negative Sampling的模型 http://www.cnblogs.com/pinard/p/7249903.html
用gensim学习word2vec http://www.cnblogs.com/pinard/p/7278324.html
https://patch-diff.githubusercontent.com/dade68/DeepLearning#action-2
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
https://patch-diff.githubusercontent.com/dade68/DeepLearning#lstm
https://patch-diff.githubusercontent.com/dade68/DeepLearning#教程-3
1. （译）理解长短期记忆(LSTM) 神经网络https://zhuanlan.zhihu.com/p/24018768
2. 一文读懂LSTM和RNNhttps://zhuanlan.zhihu.com/p/35878575?group_id=970350175025385472
3. 探索LSTM：基本概念到内部结构https://zhuanlan.zhihu.com/p/27345523
4. 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
https://patch-diff.githubusercontent.com/dade68/DeepLearning#action-3
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/dade68/DeepLearning#深度模型的优化--
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
1. [视频讲解]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
2. [视频讲解]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
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
9. 激活函数一览https://zhuanlan.zhihu.com/p/30567264
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
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/dade68/DeepLearning/blob/master
1 https://zhuanlan.zhihu.com/p/26702482
2 https://blog.csdn.net/hjimce/article/details/50866313
3 https://blog.csdn.net/malefactor/article/details/51476961
4 https://blog.csdn.net/edogawachia/article/details/80040456
5https://zhuanlan.zhihu.com/p/38176412
19. 详解深度学习中的Normalization，不只是BNhttps://zhuanlan.zhihu.com/p/33173246
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
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
https://patch-diff.githubusercontent.com/dade68/DeepLearning#炼丹术士那些事
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
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
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
不平衡数据集处理方法https://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/
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
梯度下降优化算法回顾http://ruder.io/optimizing-gradient-descent/
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/
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/dade68/DeepLearning/blob/master
1https://www.jianshu.com/p/d75375dd7ebd
2https://blog.csdn.net/kuangtun9713/article/details/79475457
https://patch-diff.githubusercontent.com/dade68/DeepLearning#机器学习深度学习基础理论
https://patch-diff.githubusercontent.com/dade68/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/dade68/DeepLearning#深度学习相关算法--
https://patch-diff.githubusercontent.com/dade68/DeepLearning#svd
1. 关于奇异值分解SVD的总结（PCA、LDI）https://zhuanlan.zhihu.com/p/30482640
2. 奇异值分解（SVD）https://zhuanlan.zhihu.com/p/29846048
3. 奇异值分解(SVD)原理详解及推导https://blog.csdn.net/zhongkejingwang/article/details/43053513
4. 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/dade68/DeepLearning#迁移学习
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/dade68/DeepLearning#域自适应
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/dade68/DeepLearning#元学习
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/dade68/DeepLearning#强化学习
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/dade68/DeepLearning#马尔科夫决策
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/dade68/DeepLearning#推荐算法---
推荐算法相关的文档整理https://zhuanlan.zhihu.com/p/29969721
https://patch-diff.githubusercontent.com/dade68/DeepLearning#自然语言处理nlp
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
YJango的Word Embedding--介绍https://zhuanlan.zhihu.com/p/27830489
CMU&谷歌大脑提出新型问答模型QANethttps://zhuanlan.zhihu.com/p/37168143
https://patch-diff.githubusercontent.com/dade68/DeepLearning#语义分割相关算法
干货 | 一文概览主要语义分割网络https://blog.csdn.net/qq_20084101/article/details/80432960
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
NMS——非极大值抑制https://blog.csdn.net/shuzfan/article/details/52711706
边框回归(Bounding Box Regression)详解https://blog.csdn.net/zijin0802034/article/details/77685438
https://patch-diff.githubusercontent.com/dade68/DeepLearning#机器学习相关算法--
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
AdaBoost到GBDT系列https://patch-diff.githubusercontent.com/dade68/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
集成学习之bagging,stacking,boosting概念理解https://zhuanlan.zhihu.com/p/41809927
https://patch-diff.githubusercontent.com/dade68/DeepLearning#决策树decision-tree-
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
决策树值CART实现https://blog.csdn.net/u014688145/article/details/53326910
https://patch-diff.githubusercontent.com/dade68/DeepLearning#支持向量机svm--
SVM通俗导论 July（本文章是我看过最好的SVM导论）https://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
Python3《机器学习实战》学习笔记（八）：支持向量机原理篇之手撕线性SVM （SMO训练过程总结得清晰易懂）https://blog.csdn.net/c406495762/article/details/78072313
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/dade68/DeepLearning#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/dade68/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/dade68/DeepLearning#蒙塔卡罗搜索树
蒙特卡洛树搜索入门指南https://zhuanlan.zhihu.com/p/34950988
https://patch-diff.githubusercontent.com/dade68/DeepLearning#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
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
https://patch-diff.githubusercontent.com/dade68/DeepLearning#集成em
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
人人都懂的EM算法 https://zhuanlan.zhihu.com/p/36331115
https://patch-diff.githubusercontent.com/dade68/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（CRF）？https://www.jianshu.com/p/55755fc649b1
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/dade68/DeepLearning#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
https://patch-diff.githubusercontent.com/dade68/DeepLearning#工具平台使用-
https://patch-diff.githubusercontent.com/dade68/DeepLearning#tensorflow
【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
https://patch-diff.githubusercontent.com/dade68/DeepLearning#mxnet
Gluonhttps://github.com/Mikoto10032/DeepLearning/blob/master/books/gluon_tutorials_zh%EF%BC%88%E5%9F%BA%E4%BA%8EMXNet%EF%BC%89.pdf
https://patch-diff.githubusercontent.com/dade68/DeepLearning#python3x
廖雪峰Python教程https://www.liaoxuefeng.com/wiki/0014316089557264a6b348958f449949df42a6d3a2e542c000
菜鸟教程http://www.runoob.com/python3/python3-tutorial.html
https://patch-diff.githubusercontent.com/dade68/DeepLearning#标注工具
labelImghttps://github.com/tzutalin/labelImg
labelmehttps://github.com/wkentaro/labelme
https://patch-diff.githubusercontent.com/dade68/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. 数据集】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
https://patch-diff.githubusercontent.com/dade68/DeepLearning#to-do-list
Inter Covariate shifthttps://blog.csdn.net/mao_xiao_feng/article/details/54317852
Transposed Convolution, Fractionally Strided Convolution or Deconvolutionhttps://buptldy.github.io/2016/10/29/2016-10-29-deconv/
Readme https://patch-diff.githubusercontent.com/dade68/DeepLearning#readme-ov-file
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