- A Tutorial on Restricted Boltzmann Machines: http://xiangjiang.live/2016/02/12/a-tutorial-on-restricted-boltzmann-machines/
- Dataset of 3D scan of 1000++ objects: http://arxiv.org/abs/1602.02481
- Train Deep Variational Autoencoders and Probabilistic Ladder Networks: http://arxiv.org/abs/1602.02282
- Dog analyzer? : https://www.what-dog.net/
- Composing Music with RNN: http://www.hexahedria.com/2015/08/03/composing-music-with-recurrent-neural-networks/
- Video about SVD made in 1976: https://www.youtube.com/watch?v=R9UoFyqJca8
- How Do Genetic Algorithms work: https://www.youtube.com/watch?v=ziMHaGQJuSI
- BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to 1 or -1: http://arxiv.org/abs/1602.02830
- Auto-scaling scikit-learn with Spark: https://databricks.com/blog/2016/02/08/auto-scaling-scikit-learn-with-spark.html
- List of ICLR accepted papers: http://www.iclr.cc/doku.php?id=iclr2016:main#accepted_papers_conference_track
- Python Implementation of Boruta: https://www.reddit.com/r/MachineLearning/comments/44qb9p/python_implementation_of_boruta_an_all_relevant/
- 50+ Data Science and Machine Learning Cheat Sheet: http://www.kdnuggets.com/2015/07/good-data-science-machine-learning-cheat-sheets.html
- Papers on Learning Neural Network Topology: https://www.reddit.com/r/MachineLearning/comments/44ld5c/interesting_papers_on_learning_automatically/
- Asynchronous Methods for Deep Reinforcement Learning: http://arxiv.org/abs/1602.01783
- Legacy Thread on Brief LSTM Explanation: https://www.reddit.com/r/MachineLearning/comments/44bxdj/scrn_vs_lstm/czp4hqr
- Resources for RNNs: https://github.com/kjw0612/awesome-rnn
- MIT’s Introduction to Probability course: https://www.edx.org/course/introduction-probability-science-mitx-6-041x-1#!
- Interactive Visualization of Artificial NN: https://github.com/drdrsh/interactive-bpann
- When Will Evolution Outperform Local Search?: http://blog.evorithmics.org/2016/01/31/when-will-evolution-outperform-local-search/
