Quick links

What has caught my attention lately:

• Distance and similarity in machine learning(Chinese). ([1])
• "How to Choose a Neural Network" from DL4J. ([2])
• Winning solution at the BCI Challenge @ NER 2015. ([3] [4])
• Winning solution of The National Data Science Bowl. Convolutional neural networks win again! A lot of techniques to prevent overfitting! ([5] [6])
• Scikit-image is a collection of algorithms for image processing. ([7])

Take away from kaggle tradeshift winner

Tradeshift competition is predicting the probability that a piece of text belongs to each of the 33 classes. Winning solutions can be found in forum thread. Code is also in git.

• Best solution is a weighted average of 14 two stage models, 13 online models and 2 simple one stage models. (blending!!!)
• Prediction of 32 labels are used as features for 2nd half of data. (Labels have strong inter-dependence!)
• Xgboost is chosen as the single metastage classifier. (Xgboost win again!)
• Not only feature analysis, but also need label analysis. 
• Feature selection for online model. 
• Heavily rely on CV and grid search to fine-tune hyper-parameters.

Some other solutions shared in forum.

• Additional (100-300) decision tree features based on Criteo's winning solution.  
• Postprocess on some labels. (o_O)
• Even 3-stages solution!
• "sklearn RandomForestClassifier active paths or ended nodes" should be useful to generate tree based features.
• Semi-supervise in blending. 

Quick links

What has caught my attention lately:

• LIBFFM: A Library for Field-aware Factorization Machines. It has been used to win two recent click-through rate prediction competitions (Criteo's and Avazu's). ([1])
• Anscombe's quartet hmm~~ :)  ([2])
• Timeseries Classification: KNN & DTW ([3] [4])
• "Outlier and Anomaly Detection In Server Instances With Machine Learning At Netflix: Cody Rioux" DBSAN + MCMC ([5])
• Introduction of python Decorators and Context Managers ([6])

Quick links

What has caught my attention lately:

• Dimensionality reduction techniques : "sparse random projections". ... "Again, random projections are not suitable for all datasets. There is no 'silver bullet' approach to dimensionality reduction." ([1] [2])
• "Ten Lessons Learned from Building (real-life impactful) Machine Learning Systems" ([3])
• NLP tool list. ([4] [5](can't open now...) )
• "Optimizing Python in the Real World" ([6])
• HBase v1.0! ([7])
• Data Science At Zillow ([8])
• "The G-means algorithm takes a hierarchical approach to detecting the number of clusters." ([9] [10])
• Comparing supervised learning algorithms ([11])