Help us understand the problem. What is going on with this article?

「ゼロから作るDeep Learning」参考文献一覧

More than 1 year has passed since last update.

017/02/13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。

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


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

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

Why do not you register as a user and use Qiita more conveniently?
  1. We will deliver articles that match you
    By following users and tags, you can catch up information on technical fields that you are interested in as a whole
  2. you can read useful information later efficiently
    By "stocking" the articles you like, you can search right away
Comments
Sign up for free and join this conversation.
If you already have a Qiita account
Why do not you register as a user and use Qiita more conveniently?
You need to log in to use this function. Qiita can be used more conveniently after logging in.
You seem to be reading articles frequently this month. Qiita can be used more conveniently after logging in.
  1. We will deliver articles that match you
    By following users and tags, you can catch up information on technical fields that you are interested in as a whole
  2. you can read useful information later efficiently
    By "stocking" the articles you like, you can search right away