September 2016
Intermediate to advanced
320 pages
4h 31m
Japanese
Content preview from 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
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参考文献
Python / NumPy
- ［1］ Bill Lubanovic：『Introducing Python』O'Reilly Media刊、2014年（邦題『入門 Python 3』斎藤康毅 監訳、長尾高弘 訳、オライリー・ジャパン刊）
- ［2］ Wes McKinney：『Python for Data Analysis』O'Reilly Media刊、2012年（邦題『Pythonによるデータ分析入門——NumPy、pandasを使ったデータ処理』小林儀匡、鈴木宏尚、瀬戸山雅人、滝口開資、野上大介 訳、オライリー・ジャパン刊）
- ［3］ Scipy Lecture Notes（http://www.turbare.net/transl/scipy-lecture-notes/index.html）
計算グラフ（誤差逆伝播法）
- ［4］ Andrej Karpathy's blog "Hacker's guide to Neural Networks"（http://karpathy.github.io/neuralnets/）
Deep Learningのオンライン授業（資料）
- ［5］ CS231n: Convolutional Neural Networks for Visual Recognition（http://cs231n.github.io/）
パラメータの更新方法
- ［6］ John Duchi, Elad Hazan, and Yoram Singer（2011）：Adaptive Subgradient Methods for Online Learning and Stochastic Optimization. Journal of Machine ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.