深度学习基础知识2

NLP之Seq2Seq
https://blog.csdn.net/qq_32241189/article/details/81591456
深度学习的seq2seq模型
https://blog.csdn.net/wangyangzhizhou/article/details/77883152
深度学习笔记(六)：Encoder-Decoder模型和Attention模型
https://blog.csdn.net/u014595019/article/details/52826423?utm_medium=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-3.compare&depth_1-utm_source=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-3.compare

三分钟带你对 Softmax 划重点
https://blog.csdn.net/red_stone1/article/details/80687921
RNN & LSTM & GRU 的原理与区别
https://www.cnblogs.com/jins-note/p/9715610.html
Seq2Seq模型 与 Attention 策略
https://www.cnblogs.com/wevolf/p/10886260.html
word2vec的详解
https://www.pianshen.com/article/53911852695/
Word Embedding理解
https://www.cnblogs.com/kjkj/p/9824419.html
https://blog.csdn.net/wzy628810/article/details/106991709
简单认识Adam优化器
https://www.jianshu.com/p/aebcaf8af76e
几种AutoEncoder原理
https://blog.csdn.net/leida_wt/article/details/85052299
VAE全面理解（上）
https://blog.csdn.net/weixin_40955254/article/details/82315224
VAE全面理解（下）
https://blog.csdn.net/weixin_40955254/article/details/82315909
https://blog.csdn.net/weixin_40955254?t=1
GAN网络详解（从零入门）
https://blog.csdn.net/LEE18254290736/article/details/97371930
生成对抗网络（GAN）教程 - 多图详解
https://blog.csdn.net/maqunfi/article/details/82220297?utm_medium=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-1.compare&depth_1-utm_source=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-1.compare
GAN的原理入门
https://www.cnblogs.com/bonelee/p/9166084.html
DCGAN、WGAN、WGAN-GP、LSGAN、BEGAN原理总结及对比
https://www.cnblogs.com/bonelee/p/9166122.html
KL散度、JS散度、Wasserstein距离
https://blog.csdn.net/leviopku/article/details/81388306
基于深度学习的目标检测技术演进：R-CNN、Fast R-CNN、Faster R-CNN
https://www.cnblogs.com/skyfsm/p/6806246.html
https://blog.csdn.net/jiongnima/article/details/79094159
YOLO文章详细解读
https://www.jianshu.com/p/13ec2aa50c12
Yolo v3目标检测网络详解
https://segmentfault.com/a/1190000021794637
https://github.com/wizyoung/YOLOv3_TensorFlow
YOLO算法详解+完整代码详解
https://blog.csdn.net/stu_shanghui/article/details/91042187
图像语义分割中的上采样(subsampling)和下采样(Upsampling)
https://blog.csdn.net/qq_37344125/article/details/108717647
深度学习之卷积神经网络CNN 常用的几个模型
https://blog.csdn.net/m0_37870649/article/details/80547167
几种经典的卷积神经网络模型
https://blog.csdn.net/yaoxunji/article/details/88351396
深度学习笔记：优化方法总结(BGD,SGD,Momentum,AdaGrad,RMSProp,Adam)
https://blog.csdn.net/u014595019/article/details/52989301
DCGAN、WGAN、WGAN-GP、LSGAN、BEGAN原理总结及对比
https://www.cnblogs.com/bonelee/p/9166122.html