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.
Linear algebra: https://www.khanacademy.org/math/linear-algebra
This is a prerequisite for machine learning.
Machine learning: https://www.coursera.org/learn/machine-learning/
If you decide having a solid knowledge of ML, taking this course will be an efficient way to do so. During this course, the assigned
matlab homeworks are suggested to be done for a better understanding.
Another ML website: https://goo.gl/itg1y8
Deep learning with less material: http://ufldl.stanford.edu/tutorial/
If you already have some basic knowledge of machine learning and desire moving to deep learning earlier, one of a great choices is to follow this tutorial which focuses on only the prerequisite of deep learning. In addition, I also suggest you to finish the related
Deep learning with rich material: http://deeplearning.net/tutorial/contents.html
This website also teaches the deep learning but with a different programing language
python for implementation. This place also contains rich details of deep learning structure and relevant theories.
Matthew D. Zeiler, Dilip Krishnan, Graham W. Taylor, Rob Fergus. Deconvolutional networks. In In CVPR. (2010).
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
Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton. ImageNet Classification with Deep Convolutional Neural Networks. 1097–1105 In Advances in Neural Information Processing Systems 25. Curran Associates, Inc., 2012. Link
Matthew D. Zeiler, Rob Fergus. Visualizing and Understanding Convolutional Networks. arXiv:1311.2901 [cs] (2013). Link
Li Deng. A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Transactions on Signal and Information Processing 3 (2014). Link
Karen Simonyan, Andrew Zisserman. Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv:1409.1556 [cs] (2014). Link