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深層学習 まとめ

[万能近似定理の説明]:

物理研究者向けのニューラルネットワーク入門 (基本編)
http://hpc-phys.kek.jp/workshop/workshop191107/tomiya_191107_lecture.pdf

[keras]:

脳死で覚えるkeras入門
https://qiita.com/wataoka/items/5c6766d3e1c674d61425

初心者のための畳み込みニューラルネットワーク(MNISTデータセット + Kerasを使ってCNNを構築)
(画像データの正規化など)
https://www.codexa.net/cnn-mnist-keras-beginner/

[VGG, GoogLeNet, ResNet]:

深層学習論文の読解(VGG)
https://qiita.com/ttomomasa/items/b673a1e0b42a2a14a9d2

畳み込みニューラルネットワークの最新研究動向 (〜2017)
https://qiita.com/yu4u/items/7e93c454c9410c4b5427

Residual Network(ResNet)の理解とチューニングのベストプラクティス
https://deepage.net/deep_learning/2016/11/30/resnet.html

 
[R-CNN, Fast R-CNN, Faster R-CNN]:

最新のRegion CNN(R-CNN)を用いた物体検出入門 ~物体検出とは? R-CNN, Fast R-CNN, Faster R-CNN, Mask R-CNN~
https://qiita.com/arutema47/items/8ff629a1516f7fd485f9

論文紹介: Fast R-CNN&Faster R-CNN
https://www.slideshare.net/takashiabe338/fast-rcnnfaster-rcnn

 
[YOLO]:
【物体検出手法の歴史 : YOLOの紹介】
https://qiita.com/mdo4nt6n/items/68dcda71e90321574a2b

 
[VAE]:
Variational Autoencoder徹底解説
https://qiita.com/kenmatsu4/items/b029d697e9995d93aa24

AutoEncoder, VAE, CVAEの比較 〜なぜVAEは連続的な画像を生成できるのか?〜
(Reparametrization Trickについても記載)
https://qiita.com/kenchin110100/items/7ceb5b8e8b21c551d69a

 
[RNN, LSTM]:

リカレントニューラルネットワークの勾配消失問題対策
(わかりやすい)
https://itisit.hateblo.jp/entry/2019/02/24/%E3%83%AA%E3%82%AB%E3%83%AC%E3%83%B3%E3%83%88%E3%83%8B%E3%83%A5%E3%83%BC%E3%83%A9%E3%83%AB%E3%83%8D%E3%83%83%E3%83%88%E3%83%AF%E3%83%BC%E3%82%AF%E3%81%AE%E5%8B%BE%E9%85%8D%E6%B6%88%E5%A4%B1%E5%95%8F

時系列データによく使用されるリカレントニューラルネットワークについて
https://kenyu-life.com/2019/03/05/recurrent_neural_network/

RNNとLSTMを理解する
http://sagantaf.hatenablog.com/entry/2019/06/04/225239

RNNの問題点とLSTMについて
https://mrsekut.site/?p=889

(記事作成中)

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