Suggested Readings for Learning "Deep"


This document lists some on-line resources for the readers who are interested in learning machine learning and deep learning. As per my experience, taking a well arranged on-line course, i.e., this one, would be the most efficient way to solidly learn machine learning. In addition, here also lists some advance materials for well understanding deep learning in terms of practical and theoretical aspects.

The Basics


Suggested Reading List

  • About the dropout operation: \cite{hintonimproving2012}
  • Typicle deep learning (AlexNet): \cite{krizhevskyimagenet2012}
  • Deconvolutional networks: \cite{zeilerdeconvolutional2010}
  • Deep learning survey: \cite{dengtutorial2014}
  • VGG model: \cite{simonyanvery2014}
  • Visualizing and Understanding: \cite{zeilervisualizing2013}
  • deeplearning website:
  • Data Mining, Big Data, and Data Science :



  1. Matthew D. Zeiler, Dilip Krishnan, Graham W. Taylor, Rob Fergus. Deconvolutional networks. (2010).

  2. Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, Ruslan R. Salakhutdinov. Improving neural networks by preventing co-adaptation of feature detectors. arXiv:1207.0580 [cs] (2012). Link

  3. Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton. ImageNet Classification with Deep Convolutional Neural Networks. 1097–1105 (2012). Link

  4. Matthew D. Zeiler, Rob Fergus. Visualizing and Understanding Convolutional Networks. arXiv:1311.2901 [cs] (2013). Link

  5. Li Deng. A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Transactions on Signal and Information Processing 3 (2014). Link

  6. Karen Simonyan, Andrew Zisserman. Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv:1409.1556 [cs] (2014). Link