Teaching Machines to Read and Comprehend (slide)

http://lxmls.it.pt/2015/lxmls15.pdf

Conclusion

Summary
* supervised machine reading is a viable research direction with the available data,
* LSTM based recurrent networks constantly surprise with their ability to encode dependencies in sequences,
* attention is a very effective and exible modelling technique.

Future directions
* more and better data, corpus querying, and cross document queries,
* recurrent networks incorporating long term and working memory are well suited to NLU task.

Lisbon Machine Learning School 2015 のスライド。トピックは自然言語処理。

Understanding Convolutional Neural Networks for NLP | WildML

http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/

In this post I’ll try to summarize what CNNs are, and how they’re used in NLP.

Deep Learningの基礎と応用

http://www.slideshare.net/beam2d/deep-learning-52872945

第14回情報科学技術フォーラム (FIT2015) のイベント企画「ビッグデータ解析のための機械学習技術」におけるチュートリアル講演資料です。ニューラルネットの基礎（計算グラフとしての定式化、勾配法、誤差逆伝播法）をさらったあと、最近画像や自然言語などで注目されている応用・手法について広く浅く紹介しています。

26 Things I Learned in the Deep Learning Summer School - Marek Rei

http://www.marekrei.com/blog/26-things-i-learned-in-the-deep-learning-summer-school/