ROUGH DRAFT authorea.com/67269
Main Data History
Export
Show Index Toggle 0 comments
  •  Quick Edit
  • Suggested Readings for Learning "Deep"

    Abstract

    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

    Others

    Suggested Reading List

    Caffe

    References

    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).