Data Data Data

:: NOTICE ::

Due to the high interest in this event, we have to change the RSVP "YES" list into a first come-first serve entry list. That means, if you are on the RSVP "Yes" list, your seat at this event is not guaranteed. The first 150 people to arrive AND who are on the "Yes" list will be admitted.

We apologize for this inconvenience. We would like to remind you that NYC Python/Learn Python NYC has events every week which are posted to their respective meetup.com pages.

Lastly, if are interested in being a venue host for our meetup, please contact organizers@nycpython.org.

THANKS:

Thanks to our sponsors!

• Venue Sponsors:

• Open Data Science Con

( http://opendatascicon.com/)

• Insight Data Science Fellows Program

( http://insightdatascience.com/)

• Food + Beverage Sponsors

• Cloudera

( http://www.cloudera.com/)

DETAILS:

Doors will open at 6:15pm, talks start at 7pm.

Talks:

• A Lightning Talk!
• Repurposing Existing Deep Learning Models: Image Similarity for fun (& profit?) -

Rajat Arya

Hear how one data scientist spent an afternoon exploiting an existing trained deep learning model, extracting deep features, and transferring them in order to build an image-based similarity service for photographs of houses.

Rajat Arya will show you don't need to be an expert in deep learning or convolutional neural networks in order to leverage deep features in your next application.

The presentation will be entirely delivered from an IPython Notebook and written in Python, using Dato for extracting deep features from a trained deep learning model and deploying the image similarity service.

After a dozen years of building scalable systems at AWS, Microsoft, AddThis, and Dato, Rajat recently started working more closely with data scientists, helping them build intelligent applications using Dato.

• Using Python at Scale for Data Science - Wes McKinney

While Python is a de-facto language for modern data engineering and data science, Python development has been confined to local data processing—thereby limiting its users to smaller data sets. Historically, to address bigger data workloads, Python developers have had to extract samples or aggregates, forcing compromises in data fidelity, adding ETL costs, and ultimately leading to a loss of productivity and addressable use cases.

Ibis, a new open source data analytics framework for Python developers, has the goal of enabling the Python data ecosystem (NumPy, pandas, etc.) to operate efficiently at Hadoop scale. To enable high performance Python at scale without the age-old JVM interoperability problems, Ibis take advantage of unique synergies between Python and Impala, the leading open source MPP analytical query engine. In this talk, Ibis creator Wes McKinney, who was also the creator of pandas, will demo the current capabilities of Ibis as well as explain its roadmap.