This Meetup is past

311 people went

Location image of event venue

Details

SF Python is bringing it's Project Night to Linkedin. It's going to be an evening of getting your questions answered, sharing your knowledge, hacking, taking tutorials, and enjoying food and drinks provided by our venue host Linkedin.

*Don’t forget: save time at check-in by registering for this event via tito.io (https://ti.to/sf-python/ProjectNightAtLinkedin), SF Python's ticketing partner

Who should attend?

• New to Python and want to work with other Pythonistas

• Experienced devs who want to hack on your work, personal or open-source projects

• Experienced devs who want to mentor others

• Anyone that's interested in our tutorial offerings

The plan:

6:00p Begin check-in

6:50p Introductions: tell us about your project and/or the kind of help you seek

7:00p Make yourself comfortable and start hacking, or attend one of the tutorials

9:30p Wrap up / Door close

**There are a few spaces for bicycle inside Minted. Once those slots are filled up, please park your bicycle outside the building.

Planned tutorials:

#1 Unit testing your python by Moshe Zadka

Learn about tools, techniques and best practices for writing unit tests. Also how to write testable code will be discussed. We will cover tox, pytest and unittest.

Moshe is a Twisted contributor, and has contributed to core Python. He loves infrastructure for building, monitoring and making services highly available.

#2 Win pong with Tensorflow and Deep Reinforcement Learning by Francesco Mosconi

In this tutorial, Francesco will walk you through deep reinforcement learning and how it can be used to train models that play video games. We will use pygame, opencv and tensorflow to train a deep neural network that is able to play the video game Pong.

Francesco Mosconi is founder of Data Weekends (http://www.dataweekends.com/) - a two days workshop on the fundamentals of Machine Learning in python.

Target audience: The tutorial is intermediate level, some prior knowledge of machine learning is required.
Pre-requisites: Install Anaconda Python, opencv, tensorflow and pygame on your machine.

# 3 PySpark Tutorial by Russell Jurney
Spark has emerged as the leading general purpose distributed data processing platform. PySpark offers a Python interface to Spark that enables all the power ofPython for data processing to come to bear when computing with Spark. Working with airline flight delay data, the tutorial will start by covering basic operations inPySpark: loading and storing data, filtering, mapping, grouping, and SQLoperations. We'll go on to tour the RDD and DataFrame APIs, showing how and when to use them. We'll learn how to prepare data and store it in different kind of databases. The class will show how to combine data flow programming andSpark SQL to slice and dice data of any size. Finally, we'll show how to use machine learning via Spark MLlib to build a predictive model to predict flight delays.

Russell Jurney is a principal consultant at Data Syndrome, a product analytics consultancy dedicated to advancing the adoption of the development methodology Agile Data Science, as outlined in the book Agile Data Science 2.0(O'Reilly, 2017). He has worked as a data scientist building data products for over a decade, starting in interactive web visualization and then moving towards full stack data products, machine learning and artificial intelligence at companies such as Ning, LinkedIn, Hortonworks and Relato. He is a self-taught visualization software engineer, data engineer, data scientist, writer and most recently, he becoming a teacher. In addition to helping companies build analytics products, Data Syndrome offers live and video training courses.

*Target audience: Beginner to intermediate level Python experience required. Some experience working with data is required, but that could be via Excel or SQL.

*Pre-requisites: The tools for the course are setup on a virtual machine that can be run locally via Vagrant/Virtualbox or remotely on an EC2 instance. Students must have a laptop with 16GB of RAM to run the virtual machine for the tutorial, otherwise an EC2 setup is available. Users wanting to setup PySpark locally on their machine will receive limited support, so this is notfor beginners.

*Don’t forget: save time at check-in by registering for this event via tito.io (https://ti.to/sf-python/ProjectNightAtLinkedin), SF Python's ticketing partner

Not interested in any of the tutorials? Bring your own project to hack on or bring your burning questions and we will try to hook you up with devs that can help you out.

Examples of projects to hack on:

• Personal side projects - your web application or personal learning project

• Open source projects - work on open issues or recruit developers for your project

• Work projects - work on anything you like and bounce ideas around

*Don’t forget: save time at check-in by registering for this event via tito.io (https://ti.to/sf-python/ProjectNightAtLinkedin), SF Python's ticketing partner

Hope to see you there!

**SF Python is run by volunteers aiming to foster the Python Community in the bay area. Please consider making a donation (https://secure.meetup.com/sfpython/contribute/) to SF Python and saying a big thank you to Linkedin for providing food, drinks, and the venue for this Wed's meetup.