another attempt to organize my ML readings ..

gistfile1.md

1. Feature Learning

• Learning Feature Representations with K-means by Adam Coates and Andrew Y. Ng
• The devil is in the details: an evaluation of recent feature encoding methods by Chatfield et. al.
• Emergence of Object-Selective Features in Unsupervised Feature Learning by Coates, Ng
• Scaling Learning Algorithms towards AI Benjio & LeCun
• A Theory of Feature Learning by Brendan van Rooyen, Robert C. Williamson

2. Deep Learning

• Dropout: A Simple Way to Prevent Neural Networks from Overfitting by Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever and Ruslan Salakhutdinov
• Understanding the difficulty of training deep feedforward neural networks by Xavier Glorot and Yoshua Bengio
• On the difficulty of training Recurrent Neural Networks by Razvan Pascanu, Tomas Mikolov and Yoshua Bengio
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift by Sergey Ioffe and Christian Szegedy
• Deep Learning in Neural Networks: An Overview by Jurgen Schmidhuber
• Qualitatively characterizing neural network optimization problems by Ian J. Goodfellow, Oriol Vinyals
• On Recurrent and Deep Neural Networks Phd thesis of Razvan Pascanu
• Scaling Learning Algorithms towards AI by Yann LeCun and Yoshua Benjio
• Efficient Backprop by LeCun, Bottou et al
• Towards Biologically Plausible Deep Learning by Yoshua Bengio, Dong-Hyun Lee, Jorg Bornschein, Zhouhan Lin
• Training Recurrent Neural Networks Phd thesis of Ilya Sutskever
• A Probabilistic Theory of Deep Learning by Ankit B. Patel, Tan Nguyen, Richard G. Baraniuk
• ImageNet Classification with Deep Convolutional Neural Networks by Krizhevsky, Sutskever and Hinton
• Text Understanding from Scratch by Xiang Zhang, Yann LeCun
• Learning Deep Architectures for AI by Yoshua Bengio
• Deep Learning - a review article that appeared in Nature by Yann LeCun, Yoshua Bengio & Geoffrey Hinton

3. Scalable Machine Learning

• Bring the Noise: Embracing Randomness is the Key to Scaling Up Machine Learning Algorithms by Brian Delssandro
• Large Scale Machine Learning with Stochastic Gradient Descent by Leon Bottou
• The TradeOffs of Large Scale Learning by Leon Bottou & Olivier Bousquet
• Hash Kernels for Structured Data by Qinfeng Shi et. al.
• Feature Hashing for Large Scale Multitask Learning by Weinberger et. al.
• Large-Scale Learning with Less RAM via Randomization by a group of authors from Google
• Collaborative Email-Spam Filtering with the Hashing-Trick by Joshua Attenberg et. al.

4. Gradient based Training

• Practical Recommendations for Gradient-Based Training of Deep Architectures by Yoshua Bengio
• Stochastic Gradient Descent Tricks by L´eon Bottou

5. Non Linear Units

• Rectified Linear Units Improve Restricted Boltzmann Machines by Nair & Hinton
• Mathematical Intuition for Performance of Rectified Linear Unit in Deep Neural Networks by Alexandre Dalyec

6. Interesting blog posts and presentations

• Hacker's Guide to Neural Networks by Andrej Karpathy
• Breaking Linear Classifiers on ImageNet by Andrej Karpathy
• Classifying plankton with Deep Neural Networks
• Deep stuff about deep learning?
• Understanding Convolution in Deep Learning
• A Brief Overview of Deep Learning by Ilya Sutskever
• Recurrent Neural Networks for Collaborative Filtering
• Deep Belief Networks vs Convolutional Neural Networks
• Deep Learning vs Probabilistic Graphical Models vs Logic
• Extracting Structured Data From Recipes Using Conditional Random Fields
• Ten Lessons Learned from Building (real-life impactful) Machine Learning Systems
• Scalable Machine Learning by Mikio L Braun
• The Unreasonable Effectiveness of Recurrent Neural Networks by Andrej Karpathy
• Initialization of deep networks
• Weak Learning, Boosting, and the AdaBoost algorithm
• Probably Approximately Correct — a Formal Theory of Learning
• Making sense of principal component analysis, eigenvectors & eigenvalues

7. Interesting courses and tutorials

• CS231n: Convolutional Neural Networks for Visual Recognition at Stanford by Andrej Karpathy
• CS224d: Deep Learning for Natural Language Processing at Stanford by Richard Socher
• STA 4273H (Winter 2015): Large Scale Machine Learning at Toronto by Russ Salakhutdinov
• AM 207 Monte Carlo Methods, Stochastic Optimization at Harvard by Verena Kaynig-Fittkau and Pavlos Protopapas
• ACL 2012 + NAACL 2013 Tutorial: Deep Learning for NLP (without Magic) at NAACL 2013 by Richard Socher, Chris Manning and Yoshua Bengio
• Video course on Deep Learning by Hugo Larochelle
• ECCV-2010 Tutorial: Feature Learning for Image Classification by Kai Yu & Andrew Ng
• Kdd 2014 Tutorial - the recommender problem revisited by Xavier Amatriain
• Machine Learning 2014-15 at Oxford University
• Course Notes - Stanford Machine Learning by Andrew Ng
• A Tutorial on Principal Components Analysis by Lindsay I Smith

8. General

• Distilling the Knowledge in a Neural Network by Geoffrey Hinton, Oriol Vinyals, Jeff Dean
• A Random Forest Guided Tour by Biau & Scornet

9. MCMC

• Markov Chain Monte Carlo Without all the Bullshit
• How would you explain Markov Chain Monte Carlo (MCMC) to a layperson?
• iTunes The Data Skeptic Podcast
• An Introduction to MCMC for Machine Learning by Christophe Andrieu, Nando De Freitas, Arnaud Daucet and Michael Jordan
• The Markov Chain Monte Carlo Revolution by Persi Diaconis

10. Conditional Random Fields

• Log-linear Models and Conditional Random Fields by Charles Elkan (video)
• Log-linear models and conditional random fields - notes by Charles Elkan