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) -Rob Schapire +Jupyter/notebook -Yoav Freund +Ulrike von Luxburg +tfidf -NLTK"

Positive matches

• 0.115 + - Class imbalance
• 0.027 + - Gradient descent
• 0.026 + - Recommender system
• 0.022 + - scikit-learn
• 0.022 + - IPython notebook
• 0.021 + - Data clustering
• 0.019 + - Learning
• 0.018 + - Text analysis
• 0.017 + - Poker
• 0.015 + - Regular expression
• 0.015 + - Ron Meir
• 0.013 + - Gensim
• 0.013 + - IPython
• 0.012 + - Artemy Kolchinsky
• 0.012 + - Pointwise mutual information
• 0.012 + - Jupyter
• 0.009 + - Privacy and Deanonymization
• 0.008 + - Future of work
• 0.008 + - Random forest
• 0.007 + - Regex

Negative matches

• 0.026 + - Natural language processing
• 0.004 + - StanfordNLP
• 0.004 + - NLP
• 0.002 + - Biomedical natural language processing
• 0.001 + - scispaCy
• 0.001 + - spaCy
• 0.001 + - CNN
• 0.000 + - Generative adversarial networks
• 0.000 + - RNN
• 0.000 + - Musical composition
• 0.000 + - Recurrent neural network
• 0.000 + - Deep generative models
• 0.000 + - Convolutional neural network
• 0.000 + - Residual leanrning
• 0.000 + - Attention
• 0.000 + - Paper/Gatys2015
• 0.000 + - Unemployment
• 0.000 + - Reinforcement learning