introduction machine

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1. 1▶8 deep neural networks are our friendsLxMLS 2016Notes
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2. 2▶9 memory networks for language understandingLxMLS 2016Notes
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3. 3▶6 syntax and parsing ILxMLS 2016Notes
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4. 4▶5 learning structured predictorsLxMLS 2016Notes
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5. 5▶7 turbo parser redux from dependencies to constituentsLxMLS 2016Notes
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6. 6▶3 sequence modelsLxMLS 2016Notes
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7. 7▶2 introduction to machine kearning linear learnersLxMLS 2016Notes
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8. 8▶4 machine translation as sequence modellingLxMLS 2016Notes
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9. 9▶Jake VanderPlas: Basic Principles of Machine Learning for Astronomy with pythonJake VanderPlas: Machine learning in Astronomy python tutorialNotes
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10. 10▶Jake VanderPlas: K-Means clustering tutorial for Astronomy in pythonJake VanderPlas: Machine learning in Astronomy python tutorialNotes
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11. 11▶Jake VanderPlas: PCA (principal component analysis) tutorial for Astronomy in pythonJake VanderPlas: Machine learning in Astronomy python tutorialNotes
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12. 12▶Jake VanderPlas: GMM (Gaussian Mixture Models) tutorial for Astronomy in pythonJake VanderPlas: Machine learning in Astronomy python tutorialNotes
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13. 13▶Jake VanderPlas: Introduction to Machine Learning for Astronomy in pythonJake VanderPlas: Machine learning in Astronomy python tutorialNotes
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14. 14▶Jake VanderPlas: Introduction to Machine Learning in Astronomy with scikit-learn in pythonJake VanderPlas: Machine learning in Astronomy python tutorialNotes
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15. 15▶Jake VanderPlas: Random Forest Classifier for Astronomy tutorial in pythonJake VanderPlas: Machine learning in Astronomy python tutorialNotes
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16. 16▶Jake VanderPlas: Random Forest Regression for Astronomy tutorial in pythonJake VanderPlas: Machine learning in Astronomy python tutorialNotes
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