Search "+Peter Norvig +Automated machine learning +Class imbalance problem +Travelling salesman problem -Probability, Paradox, and the Reasonable Person Principle +Cross validation +Cluster analysis +Artificial neural network +Data clustering +Ulrike von Luxburg +Jupyter/notebook +scikit-learn -Poker +Recommender system -Deep learning"

Positive matches

• 0.242 + - Machine learning
• 0.120 + - Class imbalance
• 0.033 + - Netflix prize
• 0.026 + - Gradient descent
• 0.021 + - IPython notebook
• 0.021 + - Brain network
• 0.018 + - Decision tree
• 0.015 + - IPython
• 0.014 + - Future of work
• 0.014 + - Python
• 0.014 + - Boosting (machine learning)
• 0.014 + - Artemy Kolchinsky
• 0.013 + - Jupyter
• 0.013 + - Random forest
• 0.012 + - tfidf
• 0.011 + - Learning
• 0.011 + - Python/Modules
• 0.011 + - skorch
• 0.010 + - tf-idf
• 0.010 + - Tim Hopper

Negative matches

• 0.021 + - Language
• 0.009 + - Residual leanrning
• 0.005 + - Amazon EC2
• 0.004 + - Reinforcement learning
• 0.003 + - Q-learning
• 0.002 + - RNN
• 0.002 + - Machine translation
• 0.002 + - Beer
• 0.002 + - Amazon Elastic Compute Cloud
• 0.002 + - Yoshua Bengio
• 0.002 + - Generative adversarial networks
• 0.002 + - Long short-term memory
• 0.002 + - word2vec
• 0.002 + - Theano
• 0.002 + - Paper/Gatys2015
• 0.002 + - Deep generative models
• 0.002 + - Dropout
• 0.002 + - Sentiment analysis
• 0.002 + - Attention (Deep learning)
• 0.002 + - Archetype