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 +tfidf +Lexical feature selection -Artemy Kolchinsky -Natural language processing"

Positive matches

• 0.097 + - Class imbalance
• 0.088 + - tf-idf
• 0.069 + - Regular expression
• 0.023 + - IPython notebook
• 0.022 + - Ulrike von Luxburg
• 0.022 + - Data clustering
• 0.022 + - Decision tree
• 0.020 + - Gradient descent
• 0.019 + - Data analysis
• 0.017 + - Recommender system
• 0.015 + - Random forest
• 0.014 + - PyTorch
• 0.014 + - Pointwise mutual information
• 0.013 + - Python/Modules
• 0.012 + - IPython
• 0.012 + - Jupyter
• 0.012 + - Python
• 0.011 + - SSH tunneling
• 0.011 + - skorch
• 0.010 + - Future of work

Negative matches

• 0.011 + - CNN
• 0.007 + - Residual Learning
• 0.005 + - Residual leanrning
• 0.003 + - RNN
• 0.002 + - BERT
• 0.002 + - String metric
• 0.002 + - fastText
• 0.002 + - Word segmentation
• 0.002 + - Word embedding
• 0.002 + - Kyunghyun Cho
• 0.002 + - KoNLPy
• 0.002 + - Differentiable neural computers
• 0.002 + - spaCy
• 0.002 + - Probability
• 0.002 + - Sentiment analysis
• 0.002 + - Archetype
• 0.002 + - Sentence embedding
• 0.002 + - Latent Dirichlet allocation
• 0.001 + - Christopher D. Manning
• 0.001 + - KenLM