Search "+Peter Norvig +Automated machine learning +Class imbalance problem +Travelling salesman problem -Probability, Paradox, and the Reasonable Person Principle +Cross validation +Cluster analysis +Machine learning +Machine learning for healthcare +tf-idf +Boosting (machine learning) +Gensim +Ulrike von Luxburg +Gradient descent +Learning +IPython notebook -Neural network"

Positive matches

• 0.116 + - Class imbalance
• 0.030 + - tfidf
• 0.029 + - Recommender system
• 0.024 + - Text analysis
• 0.023 + - word2vec
• 0.022 + - Lexical feature selection
• 0.022 + - scikit-learn
• 0.021 + - Data clustering
• 0.021 + - Deep learning
• 0.020 + - Jupyter/notebook
• 0.020 + - Poker
• 0.020 + - Neuron
• 0.018 + - Pointwise mutual information
• 0.016 + - Yoav Freund
• 0.016 + - Ron Meir
• 0.015 + - Regular expression
• 0.015 + - How to study
• 0.015 + - Michael Ramscar
• 0.015 + - Rob Schapire
• 0.012 + - NLTK

Negative matches

• 0.016 + - Paper/Srivastava2014
• 0.007 + - Jack Terwilliger
• 0.006 + - Ilya Sutskever
• 0.006 + - Neural Turing machine
• 0.006 + - Dropout
• 0.006 + - Recurrent neural network
• 0.005 + - Liquid state machine
• 0.003 + - Tomas Mikolov
• 0.003 + - Graph embedding
• 0.002 + - Skip-gram
• 0.001 + - Google
• 0.001 + - Paper/Graves2014NTM
• 0.001 + - Attention (Deep learning)
• 0.001 + - Wolfgang Maass
• 0.001 + - Ivo Danihelka
• 0.001 + - Alex Graves
• 0.001 + - Greg Wayne
• 0.001 + - wiki2vec
• 0.001 + - Paper/Bahdanau2015
• 0.001 + - Paper/Levy2014