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The Future of NLP in Python (Keynote, PyCon Col...

The Future of NLP in Python (Keynote, PyCon Colombia 2020)

Ines Montani

February 08, 2020
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  1. Problem #1 Local AI startup’s code base “kind of hard

    to read” Matt (25, Senior Engineer): “array[:, ..., :4] – what does this even mean?” BREAKING
  2. Problem #3 WE NEED A DATABASE OF COMPANY ACQUISITIONS WITH

    PRICES AND STOCK TICKERS. pytorch predict company acquisitions with prices and stock tickers No results. OKAY, I'M ON IT!
  3. TEXT CLASSIFIER ENTITY RECOGNIZER ENTITY LINKER ATTRIBUTE LOOKUP Microsoft acquires

    software development platform GitHub for $7.5 billion
  4. TEXT CLASSIFIER ENTITY RECOGNIZER ENTITY LINKER ATTRIBUTE LOOKUP CURRENCY NORMALIZER

    Microsoft acquires software development platform GitHub for $7.5 billion
  5. Pope Francis visits U.S. Which is CORRECT? P E R

    S O N Pope Francis visits U.S. P E R S O N
  6. S W A M P O F UN C E

    R TAIN T Y Effort (training data size, time, experimenting) Effectiveness (accuracy, quality) typical project
  7. H I L L O F H O P E

    S W A M P O F UN C E R TAIN T Y Effort (training data size, time, experimenting) Effectiveness (accuracy, quality) typical project
  8. P L A T E A U O F F

    R U S T R A T I O N H I L L O F H O P E S W A M P O F UN C E R TAIN T Y Effort (training data size, time, experimenting) Effectiveness (accuracy, quality) typical project
  9. S W A M P O F UN C E

    R TAIN T Y Effort (training data size, time, experimenting) Effectiveness (accuracy, quality) typical project
  10. Q U I C K S A N D O

    F S U N K C O S T S S W A M P O F UN C E R TAIN T Y Effort (training data size, time, experimenting) Effectiveness (accuracy, quality) typical project
  11. Q U I C K S A N D O

    F S U N K C O S T S S W A M P O F UN C E R TAIN T Y Effort (training data size, time, experimenting) Effectiveness (accuracy, quality) typical project when TO STOP?
  12. I T E R A T I V E W

    E T L A N D S O F S L I G H T L Y L E S S UN C E R TAIN T Y Effort (training data size, time, experimenting) Effectiveness (accuracy, quality) future project
  13. I T E R A T I V E W

    E T L A N D S O F S L I G H T L Y L E S S UN C E R TAIN T Y Effort (training data size, time, experimenting) Effectiveness (accuracy, quality) future project STOP STOP
  14. M E A D O W S O F S

    U C C E S S GO! I T E R A T I V E W E T L A N D S O F S L I G H T L Y L E S S UN C E R TAIN T Y Effort (training data size, time, experimenting) Effectiveness (accuracy, quality) future project STOP STOP