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 -Poker +Jupyter/notebook +Regex +scikit-learn +Python/Modules +skorch +Gradient descent -Neural network"

Positive matches

• 0.098 + - Class imbalance
• 0.070 + - Regular expression
• 0.035 + - PyTorch
• 0.029 + - Decision tree
• 0.023 + - Ulrike von Luxburg
• 0.023 + - IPython notebook
• 0.022 + - Data clustering
• 0.022 + - tfidf
• 0.022 + - tf-idf
• 0.020 + - Neuron
• 0.018 + - Recommender system
• 0.017 + - IPython
• 0.016 + - Random forest
• 0.016 + - Python
• 0.014 + - Boosting (machine learning)
• 0.013 + - Artemy Kolchinsky
• 0.012 + - Jupyter
• 0.012 + - Lexical feature selection
• 0.012 + - Tim Hopper
• 0.010 + - Future of work

Negative matches

• 0.018 + - Paper/Srivastava2014
• 0.008 + - Jack Terwilliger
• 0.006 + - Liquid state machine
• 0.006 + - Neural Turing machine
• 0.006 + - word2vec
• 0.005 + - Recurrent neural network
• 0.005 + - Dropout
• 0.004 + - Ilya Sutskever
• 0.004 + - Graph embedding
• 0.003 + - Probability
• 0.003 + - Hierarchical softmax
• 0.003 + - wiki2vec
• 0.002 + - Skip-gram
• 0.002 + - WordRank
• 0.002 + - sense2vec
• 0.002 + - Google
• 0.002 + - Attention (Deep learning)
• 0.001 + - Gensim
• 0.001 + - Paper/Levy2014
• 0.001 + - Paper/Hamilton2016