A practical guide to training restricted boltzmann machines (2010), G. Hinton Understanding the difficulty of training deep feedforward neural networks (2010), X. Glorot and Y. Bengio

Why does unsupervised pre-training help deep learning (2010), D. Erhan et al

Recurrent neural network based language model (2010), T. Mikolov et al.

Learning deep architectures for AI (2009), Y. Bengio.

Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations (2009), H. Lee et al.

Greedy layer-wise training of deep networks (2007), Y. Bengio et al

Reducing the dimensionality of data with neural networks, G. Hinton and R. Salakhutdinov

A fast learning algorithm for deep belief nets (2006), G. Hinton et al.

Gradient-based learning applied to document recognition (1998), Y. LeCun et al

Long short-term memory (1997), S. Hochreiter and J. Schmidhuber.

　　HW/SW/数据集

OpenAI gym (2016), G. Brockman et al

TensorFlow: Large-scale machine learning on heterogeneous distributed systems (2016), M. Abadi et al.

Theano: A Python framework for fast computation of mathematical expressions, R. Al-Rfou et al.

MatConvNet: Convolutional neural networks for matlab (2015), A. Vedaldi and K. Lenc

Imagenet large scale visual recognition challenge (2015), O. Russakovsky et al

Caffe: Convolutional architecture for fast feature embedding (2014), Y. Jia et al

　　书籍/调查/概述

Deep learning (Book, 2016), Goodfellow et al.

LSTM: A search space odyssey (2016), K. Greff et al.

Deep learning (2015), Y. LeCun, Y. Bengio and G. Hinton

Deep learning in neural networks: An overview (2015), J. Schmidhuber Representation learning: A review and new perspectives (2013), Y. Bengio et al.

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