- 01 Jun 2017 » Day97: Looking deep
- 31 May 2017 » Day96: Boosting
- 30 May 2017 » Day95: Random Forest & Extremely Randomized Trees
- 29 May 2017 » Day94: Bagging
- 28 May 2017 » Day93: Regression trees
- 24 May 2017 » Day89: Lasso
- 23 May 2017 » Day88: Ridge Regression
- 01 May 2017 » Day66: Hierarchical clustering
- 21 Apr 2017 » Day56: Finding the similar documents
- 20 Apr 2017 » Day55: Minimizing document distances
- 18 Apr 2017 » Day53: SVD in Scikit-learn
- 16 Apr 2017 » Day51: Basic SVD
- 31 Mar 2017 » Day35: Using connection weights
- 30 Mar 2017 » Day34: Getting the variable importance
- 29 Mar 2017 » Day33: Garson's algorithm
- 28 Mar 2017 » Day32: Variable importance in ANNs
- 27 Mar 2017 » Day31: Saving neural networks
- 26 Mar 2017 » Day30: A working CNN
- 25 Mar 2017 » Day29: CNN structure
- 24 Mar 2017 » Day28: Modularization (Python)
- 23 Mar 2017 » Day27: Simple neural network in Lasagne
- 22 Mar 2017 » Day26: Theano & Lasagne
- 21 Mar 2017 » Day25: Evaluate with Kaggle
- 20 Mar 2017 » Day24: Transforming categorical variables
- 19 Mar 2017 » Day23: Using Seaborn & Linear Regression
- 18 Mar 2017 » Day22: Splitting data, calculating metrics, cross-validations
- 17 Mar 2017 » Day21: Cross-validation
- 16 Mar 2017 » Day20: Important features
- 09 Mar 2017 » Day13: K-means clustering
- 06 Mar 2017 » Day10: Feature selection
- 04 Mar 2017 » Day09: Back to Kaggle and neural networks
- 01 Mar 2017 » Day05: Recognition with random forest
- 27 Feb 2017 » Day03: APIs and sentiment analysis
- 26 Feb 2017 » Day02: Neural network
- 25 Feb 2017 » Day01: Kaggle competition