「ゼロから作るDeep Learning」参考文献一覧 python(9)

2017/02/13に作成した	
「ゼロから作るDeep Learning」参考文献一覧等
https://researchmap.jp/joxn1ul6v-2078500/#_2078500
をQiitaに移転作業中です。参考文献の参考文献を作成予定です。

 <この項は書きかけです。順次追記します。>

ゼロから作るDeep Learning ―Pythonで学ぶディープラーニングの理論と実装
斎藤 康毅
オライリージャパン(2016/09/24)
https://www.oreilly.co.jp/books/9784873117584/

参考文献一覧

１
Introducing Python
Bill Lubanovic
Oreilly & Associates Inc(2014/12/04)


入門 Python 3
Bill Lubanovic
オライリージャパン(2015/12/01)

2

Python for Data Analysis
Wes Mckinney
Oreilly & Associates Inc(2012/10/29)


Python for Data Analysis: Data Wrangling With Pandas, Numpy, and Ipython
Wes Mckinney
Oreilly & Associates Inc(2017/07/25)


Pythonによるデータ分析入門 ―NumPy、pandasを使ったデータ処理
Wes McKinney
オライリージャパン(2013/12/26)

3
 Scipy Lecture Notes
http://www.scipy-lectures.org

4
Andrej Karpathys blog, Hacker's guide to Neural Networks
http://karpathy.github.io/neuralnets/

5
CS231n: Convolutional Neural Networks for Visual Recognition
http://cs231n.github.io
http://cs231n.stanford.edu

6
John Duchi 
Adaptive Subgradient Methods for
Online Learning and Stochastic Optimization, Journal of Machine Learning Research 12 (2011) 2121-2159

7 Lecture 6.5 — Rmsprop: normalize the gradient 

8
Adam: A Method for Stochastic Optimization

9
understanding the difficulty of training deep feedforward neural networks

10
delving deep into rectifiers surpassing human-level performance on imagenet

11
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

12
all you need is a good init

13
Understanding the backward pass through Batch Normalization Layer
https://kratzert.github.io/2016/02/12/understanding-the-gradient-flow-through-the-batch-normalization-layer.html

14
dropout a simple way to prevent neural networks from overfitting

15
Random search for hyper parameter optimization , F, P

16
practical bayesian optimization of machine learning algorithms , F, P

17
visualizing and understanding convolutional networks , F, P

18
 understanding deep image representations by inverting them F,P

19
a matlab plugin to  visualize neurons from deep models
http://vision03.csail.mit.edu/cnn_art/

20
gradient based learning applied to document recognition

21
imagenet classification with deep convolutional neural networks

22
very deep convolutional networks for large-scale image recognition

23
going deeper with convolutions

24
deep residual learning for image recognition

25
imagenet a large-scale hierarchical image database

26 
learning schematic image representation at a large scale, F
http://www.eecs.berkeley.edu/Pubs/TechRpts/2014/EECS-2014-93.html

27
NVIDIA Propels Deep Learning with TITAN X, New Digits training system and DevBox
https://blogs.nvidia.com/blog/2015/03/17/digits-devbox/

28
Announcing TensorFlow 0.8 – now with distributed computing support!
https://research.googleblog.com/2016/04/announcing-tensorflow-08-now-with.html

29
Tensor flow: large scale machine learning on heterogeneous distributed system

30
deep learning with limited numerical precision

31
binarized neural networks training deep neural networks with weights and activations constrained to +1 or -1.
https://github.com/MatthieuCourbariaux/BinaryNet

32
classification datasets results, Rodrigo benenson
http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html

33
regularization of neural networks using dropconnect ICML2013

34
Visual Object classes challenge 2012
http://host.robots.ox.ac.uk/pascal/VOC/voc2012/

35
https://people.eecs.berkeley.edu/~rbg/papers/r-cnn-cvpr-supp.pdf

36
faster r-cnn towards real-time object detection

37
fully convolutional networks for semantic segmentation

38
Show and Tell: A Neural Image Caption Generator

39.
 A neural algorithm of artistic style

40
jcjohnson/neural-style

41
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

42
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://arxiv.org/pdf/1511.00561.pdf

43
segnet  demo page

44
Human-level control through deep reinforcement learning
https://web.stanford.edu/class/psych209/Readings/MnihEtAlHassibis15NatureControlDeepRL.pdf

45
Mastering the game of Go with deep neural networks and tree search
~~https://gogameguru.com/i/2016/03/deepmind-mastering-go.pdf~~
https://storage.googleapis.com/deepmind-media/alphago/AlphaGoNaturePaper.pdf

## 一覧
物理記事　上位100
https://qiita.com/kaizen_nagoya/items/66e90fe31fbe3facc6ff

量子(0) 計算機, 量子力学 
https://qiita.com/kaizen_nagoya/items/1cd954cb0eed92879fd4

数学関連記事１００
https://qiita.com/kaizen_nagoya/items/d8dadb49a6397e854c6d

言語・文学記事　１００
https://qiita.com/kaizen_nagoya/items/42d58d5ef7fb53c407d6

医工連携関連記事一覧
https://qiita.com/kaizen_nagoya/items/6ab51c12ba51bc260a82

自動車　記事　１００
https://qiita.com/kaizen_nagoya/items/f7f0b9ab36569ad409c5

通信記事１００
https://qiita.com/kaizen_nagoya/items/1d67de5e1cd207b05ef7

日本語（０）一欄
https://qiita.com/kaizen_nagoya/items/7498dcfa3a9ba7fd1e68

英語(0) 一覧
https://qiita.com/kaizen_nagoya/items/680e3f5cbf9430486c7d

転職(0)一覧
https://qiita.com/kaizen_nagoya/items/f77520d378d33451d6fe

仮説（0）一覧（目標100現在40）
https://qiita.com/kaizen_nagoya/items/f000506fe1837b3590df

Qiita(0)Qiita関連記事一覧（自分）
https://qiita.com/kaizen_nagoya/items/58db5fbf036b28e9dfa6

鉄道（０）鉄道のシステム考察はてっちゃんがてつだってくれる
https://qiita.com/kaizen_nagoya/items/26bda595f341a27901a0

安全（0）安全工学シンポジウムに向けて: 21
https://qiita.com/kaizen_nagoya/items/c5d78f3def8195cb2409

一覧の一覧( The directory of directories of mine.) Qiita(100)
https://qiita.com/kaizen_nagoya/items/7eb0e006543886138f39

Ethernet 記事一覧　Ethernet(0)
https://qiita.com/kaizen_nagoya/items/88d35e99f74aefc98794

Wireshark 一覧 wireshark(0)、Ethernet(48) 
https://qiita.com/kaizen_nagoya/items/fbed841f61875c4731d0

線網（Wi-Fi）空中線(antenna)(0) 記事一覧(118/300目標)
https://qiita.com/kaizen_nagoya/items/5e5464ac2b24bd4cd001

OSEK OS設計の基礎　OSEK(100)
https://qiita.com/kaizen_nagoya/items/7528a22a14242d2d58a3

Error一覧 error(0)
https://qiita.com/kaizen_nagoya/items/48b6cbc8d68eae2c42b8

++ Support(0)　
https://qiita.com/kaizen_nagoya/items/8720d26f762369a80514

Coding(0) Rules, C, Secure, MISRA and so on
https://qiita.com/kaizen_nagoya/items/400725644a8a0e90fbb0

プログラマによる、プログラマのための、統計(0)と確率のプログラミングとその後
https://qiita.com/kaizen_nagoya/items/6e9897eb641268766909

なぜdockerで機械学習するか 書籍・ソース一覧作成中 (目標100)
https://qiita.com/kaizen_nagoya/items/ddd12477544bf5ba85e2

言語処理100本ノックをdockerで。python覚えるのに最適。:10+12
https://qiita.com/kaizen_nagoya/items/7e7eb7c543e0c18438c4

プログラムちょい替え（0）一覧:4件
https://qiita.com/kaizen_nagoya/items/296d87ef4bfd516bc394

官公庁・学校・公的団体（NPOを含む）システムの課題、官（０）
https://qiita.com/kaizen_nagoya/items/04ee6eaf7ec13d3af4c3

「はじめての」シリーズ　 ベクタージャパン　
https://qiita.com/kaizen_nagoya/items/2e41634f6e21a3cf74eb

AUTOSAR(0)Qiita記事一覧, OSEK(75)
https://qiita.com/kaizen_nagoya/items/89c07961b59a8754c869

プログラマが知っていると良い「公序良俗」
https://qiita.com/kaizen_nagoya/items/9fe7c0dfac2fbd77a945

LaTeX(0) 一覧　
https://qiita.com/kaizen_nagoya/items/e3f7dafacab58c499792

自動制御、制御工学一覧（０）
https://qiita.com/kaizen_nagoya/items/7767a4e19a6ae1479e6b

Rust(0) 一覧　
https://qiita.com/kaizen_nagoya/items/5e8bb080ba6ca0281927

小川清最終講義、最終講義（再）計画, Ethernet(100) 英語(100) 安全(100)
https://qiita.com/kaizen_nagoya/items/e2df642e3951e35e6a53

＜この記事は個人の過去の経験に基づく個人の感想です。現在所属する組織、業務とは関係がありません。＞
This article is an individual impression based on the individual's experience. It has nothing to do with the organization or business to which I currently belong.
### 文書履歴
ver. 0.10 初稿 20170213
ver. 0.11 追記　20180805
ver. 0.12 45URL変更, 42,44追記 20181008
ver. 0.13 ありがとう追記 　 20230527
### 最後までおよみいただきありがとうございました。
いいね　💚、フォローをお願いします。
####  Thank you very much for reading to the last sentence.
Please press the like icon 💚　and follow me for your happy life.