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)
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.
32
classification datasets results, Rodrigo benenson
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
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
＜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。＞
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.