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PyData NYC 2019

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2020-04-01 07:06:53

https://www.icourse163.org/ 欢迎大家为我投币，充电哦^_^

GalileoHua 发消息

我爱编程，编程爱我

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1万人感兴趣

1.Aditi Khullar, Eugene Tang - Introduct

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2.Aditya Lahiri - Dealing With Imbalance

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3.Akos Furton - Production Code in Data

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4.Alex Egg, Emily A Ray, Parin Choganwal

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5.Allen Downey - The Inspection Paradox

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6.Amanda Moran - Pandas vs Koalas - The

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7.Anthony Scopatz - Conda-press, or Rein

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8.Bill Lynch - Bringing mental health da

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9.Brian Austin - How and why to put your

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10.Cameron Davidson-Pilon - New Trends i

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11.Carlos Afonso - Visualizing the 2019

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12.Chaya D Stern, Yuanqing Wang - Using

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13.Chris Fonnesbeck - A Primer on Gaussi

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14.Chris Wiggins, Anne Bauer - Data scie

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15.Colin Carroll, Hannah Aizenman, Thoma

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16.Daniel Rodriguez - Effective Python a

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17.Diego Torres Quintanilla - Cleaning,

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18.Eric Dill - Is Spark still relevant M

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19.Ethan Rosenthal - Time series for sci

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20.Evan Patterson - Semantic modeling of

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21.Ferras Hamad - Reproducibility in ML

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22.Francesc Alted, Christian Steiner - I

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23.Hillary Green-Lerman, Michoel Snow -

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24.Ian Whalen - Implementing Lightweight

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25.Itamar Turner-Trauring - Small Big Da

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26.Jacqueline Gutman - Simplified Data Q

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27.James Powell - Sloth & ENVy

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28.Jenny Turner-Trauring - Working with

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29.Jens Fredrik Skogstrom - What we lear

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30.Jessica Tyler - Geo Experiments and C

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31.Joe Jevnik - Zarr vs. HDF5

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32.Joshua Falk - Generating realistic, d

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33.Julia Signell - Data-centric explorat

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34.Kamal Abdelrahman - Painting a Pictur

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35.Katherine Kampf - Build an AI-powered

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36.Keith Kraus - High-Performance Data S

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37.Kelly Jin - A Few Good Public Servant

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38.Kelly Shen - Julia for Pythonistas

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39.Lauren Oldja - Managing Stakeholders

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40.Li Jin, Hyonjee Joo - Spark Backend f

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41.Maciej Wojton - Free Your Esoteric Da

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42.Malaika Handa - Colorism in High Fash

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43.Marc Garcia, Jeff Reback, Tom Augspur

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44.Marianne Hoogeveen - The physics of d

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45.Mariel Frank - Introduction to NLP

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46.Marius van Niekerk, Rohit Kapur - A H

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47.Martin Hirzel - Type-Driven Automated

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48.Matti Lyra - Neural Networks for Natu

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49.Meghan Heintz - Launching a new wareh

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50.Michael Johns - Propensity Score Matc

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51.Michoel Snow, Hillary Green-Lerman -

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52.Mitzi Morris - Bayesian Inference for

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53.Moussa Taifi - Clean Machine Learning

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54.Noam Ross - Building Software and Com

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55.Patrick Landreman - A Crash Course in

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56.Piero Ferrante - Should I develop my

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57.Raman Tehlan - Genetic algorithms - M

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58.Raoul-Gabriel Urma - Advanced Softwar

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59.Raphaël Meudec - tf-explain - Interp

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60.Romain Cledat - Every ML Model Deserv

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61.Samuel Rochette - Quantifying uncerta

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62.Sara Seager - Stars, Planets, and Pyt

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63.Saul Shanabrook - Same API, Different

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64.Stanley van der Merwe, Petr Wolf - Fr

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65.Steve Dower - The Secret Life of Pyth

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66.Sumowska, Desikan, Warner, Konstantin

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67.Tamar Yastrab - The Echo-Chamber of Y

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68.This AI Clones Your Voice After Liste

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69.Thomas J Fan - Deep Dive into scikit-

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70.Tom Augspurger - Scalable Machine Lea

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71.Veronica Hanus - To comment or not

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72.Will Kurt - An Introduction to Probab

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