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SAT

auditorium 9am

brilliant intro by emlyn & ruby then keynote, automated fact checking https://pydata.org/london2017/schedule/presentation/66/ https://fullfact.org/about/our-team/

lg6 10am

static type analysis https://speakerdeck.com/marcobonzanini/static-type-analysis-for-robust-data-products-at-pydata-london-2017

pip install mypy http://mypy-lang.org/

lg7 11am

random forests https://pydata.org/london2017/schedule/presentation/25/ https://github.com/NathanEpstein/pydata-london https://codewords.recurse.com/issues/seven/a-tour-of-random-forests https://spark.apache.org/docs/2.1.0/mllib-ensembles.html

auditorium 1.30pm

machine learning for artists gene kogan, http://ml4a.github.io/ https://pydata.org/london2017/schedule/presentation/67/ http://genekogan.com/ https://github.com/phillipi/pix2pix https://github.com/ibab/tensorflow-wavenet

auditorium 2.30pm

data and diversity in the music industry https://pydata.org/london2017/schedule/presentation/39/ meh

FRI

auditorium 10am

https://pydata.org/london2017/schedule/presentation/10/ andryichenko, jupyter https://github.com/rs2/pydata-2017-jupyter-workshop

auditorium 12am

https://pydata.org/london2017/schedule/presentation/5/ zeigerman, tensorflow conv nets https://github.com/DJCordhose/speed-limit-signs/blob/master/README.md https://notebooks.azure.com/anon-oojjea/libraries/pydata

dining 2.30pm

https://pydata.org/london2017/schedule/presentation/2/ radcliffe, tdda http://www.tdda.info/pdf/tdda-tutorial-pydata-london-2017.pdf

# references
python -m tdda.referencetest.examples
python test_using_referencetestcase.py

# constraints
python -m tdda.constraints.examples
python elements_discover_92.py
tdda verify testdata/elements118.csv elements118.tdda

# then some blah about an automatic regex generator called rexpy

dining 4pm

https://pydata.org/london2017/schedule/presentation/14/ analysis with react blah

source live coded in bit.ly/dutc-tutorial (dropbox)

to check

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