Here are some materials for deep learning.
(1) Deep Learning by Yoshua Bengio, Ian Goodfellow and Aaron Courville
(2) Neural Network and Deep Learning (Book, Jan 2017), Michael Nielsen.
Machine Learning - Stanford by Andrew Ng in Coursera
(1) UFLDL Neural Network
(2) A Deep Learning Tutorial: From Perceptrons to Deep Networks
 LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. “Deep learning.” Nature 521.7553 (2015): 436-444.
 Deep Learning in Neural Networks: An Overview
 On the Origin of Deep Learning
(1) GAN model
 Goodfellow, Ian, et al. “Generative adversarial nets.” Advances in Neural Information Processing Systems. 2014.
(2) Autoencoder model
 Hinton, Geoffrey E., and Ruslan R. Salakhutdinov. “Reducing the dimensionality of data with neural networks.” science 313.5786 (2006): 504-507.
(3) Convolutional neural networks(CNN) in Image
 Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. “Imagenet classification with deep convolutional neural networks.” Advances in neural information processing systems. 2012.
(4) Deep residual learning model
 He, Kaiming, et al. “Deep residual learning for image recognition.” arXiv preprint arXiv:1512.03385 (2015) Link: https://arxiv.org/abs/1512.03385
(5) Sequence-to-Sequence Model in Natual Language Processing(NLP)
Sutskever, Ilya, Oriol Vinyals, and Quoc V. Le. “Sequence to sequence learning with neural networks.” Advances in neural information processing systems. 2014.