Interesting list published on Startup.ml. Here are the main categories:
- Deep Learning
- Online Learning
- Graphical Models
- Structured Predictions
- Ensemble Methods
- Kernel Machines
- Hyper-parameter Optimization
- Hadoop / Spark
- GPU learning
- Natural Language Processing
- Computer Vision
Each section features software, research and lectures. Here's their selection for deep learning:
- Vowpal Wabbit (VW) – a fast out-of-core learning system that serves as a research vehicle on online learning, reductions, cluster parallel and other areas.
- Sofia-ML – Suite of Fast Incremental Algorithms for Machine Learning. Includes methods for learning classification and ranking models, using Pegasos SVM, SGD-SVM, ROMMA, Passive-Aggressive Perceptron, Perceptron with Margins, and Logistic Regression.
- Agarwal, Alekh, et al. "A reliable effective terascale linear learning system." arXiv preprint arXiv:1110.4198 (2011).
- Langford, John, LeCun, Yann. "Large-Scale Machine Learning"
A few categories are missing, including feature selection, scoring models, neural networks, and association rules. But it's a great resource nevertheless.
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