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Positive matches

• 0.201 + - Machine learning
• 0.127 + - Class imbalance
• 0.036 + - Public health policy
• 0.036 + - Netflix prize
• 0.036 + - Simpson's paradox
• 0.029 + - Interdisciplinary research
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• 0.025 + - Gradient descent
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• 0.011 + - Boosting (machine learning)
• 0.011 + - Bookshelf
• 0.011 + - Industry-Academic Partnership
• 0.010 + - Privacy and Deanonymization
• 0.008 + - Programming interview
• 0.006 + - scikit-learn

Negative matches

• 0.018 + - Michael Ramscar
• 0.017 + - How to study
• 0.013 + - Deep learning
• 0.002 + - Jonah Lehrer
• 0.002 + - 유재명
• 0.002 + - Autoencoder
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• 0.002 + - Practice
• 0.002 + - word2vec
• 0.002 + - Marc'Aurelio Ranzato
• 0.002 + - Yoshua Bengio
• 0.002 + - Jürgen Schmidhuber
• 0.002 + - Generative adversarial network
• 0.002 + - Residual Learning
• 0.001 + - Residual leanrning
• 0.001 + - Differentiable neural computers
• 0.001 + - Python#Modules_.26_Libraries