BEGIN:VCALENDAR VERSION:2.0 PRODID:icalendar-ruby CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/Los_Angeles BEGIN:DAYLIGHT DTSTART:20170312T030000 TZOFFSETFROM:-0800 TZOFFSETTO:-0700 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:PDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20161106T010000 TZOFFSETFROM:-0700 TZOFFSETTO:-0800 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:PST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260602T083748Z UID:HvC3Aw DTSTART;VALUE=DATE:20170215 DTEND;VALUE=DATE:20170216 CLASS:PUBLIC CREATED:20170202T155705 DESCRIPTION: On Feb 15\, SF Python is bringing it's Project Night to Linke din. It's going to be an evening with about 150 Python Devs sharing knowl edge\, getting questions answered\, hacking together\, and enjoying food a nd drinks provided by our venue host Linkedin. \n\n Who should attend? \n \n • New to Python and want to work with other Pythonistas \n\n • Exp erienced devs who want to hack on your work\, personal or open-source proj ects \n\n • Experienced devs who want to mentor others \n\n • Anyone that's interested in our tutorial offerings \n\n The plan: \n\n 6:00p Beg in check-in \n\n 6:50p Introductions: tell us about your project and/or th e kind of help you seek \n\n 7:00p Make yourself comfortable and start hac king\, or attend one of the tutorials \n\n 9:30p Wrap up / Door close \n\n Planned tutorials: \n\n #1 Unit testing your python by Moshe Zadka \n\n L earn about tools\, techniques and best practices for writing unit tests. A lso how to write testable code will be discussed. We will cover tox\, pyt est and unittest. \n\n Moshe is a Twisted contributor\, and has contribute d to core Python. He loves infrastructure for building\, monitoring and ma king services highly available. \n\n #2 Win pong with Tensorflow and Dee p Reinforcement Learning by Francesco Mosconi \n\n In this tutorial\, Fran cesco 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 an d tensorflow to train a deep neural network that is able to play the video game Pong. \n\n \n\n Francesco Mosconi is founder of Data Weekends - a t wo days workshop on the fundamentals of Machine Learning in python. \n\n Target audience: The tutorial is intermediate level\, some prior knowledg e of machine learning is required. \nPre-requisites: Install Anaconda Pyth on\, opencv and pygame on your machine. \n\n # 3 PySpark Tutorial by Russe ll Jurney \n\n Spark has emerged as the leading general purpose distribute d data processing\nplatform. PySpark offers a Python interface to Spark th at enables all the power of\nPython for data processing to come to bear wh en computing with Spark. Working\nwith airline flight delay data\, the tut orial will start by covering basic operations in\nPySpark: loading and sto ring data\, filtering\, mapping\, grouping\, and SQL\noperations. We'll go on to tour the RDD and DataFrame APIs\, showing how and\nwhen to use them . We'll learn how to prepare data and store it in different kinds\nof data bases. The class will show how to combine data flow programming and\nSpark SQL to slice and dice data of any size. Finally\, we'll show how to use\n machine learning via Spark MLlib to build a predictive model to predict fl ight\ndelays. \n\n Russell Jurney is principal consultant at Data Syndrome \, a product analytics\nconsultancy dedicated to advancing the adoption of the development\nmethodology Agile Data Science\, as outlined in the book Agile Data Science 2.0\n(O'Reilly\, 2017). He has worked as a data scient ist building data products for over\na decade\, starting in interactive we b visualization and then moving towards fullstack\ndata products\, machine learning and artificial intelligence at companies\nsuch as Ning\, LinkedI n\, Hortonworks and Relato. He is a self taught visualization\nsoftware en gineer\, data engineer\, data scientist\, writer and most recently\, he's\ nbecoming a teacher. In addition to helping companies build analytics prod ucts\,\nData Syndrome offers live and video training courses. \n\n Target audience: Beginner to intermediate level Python experience required. Some experience working with data is required\, but that could be via Excel or SQL. \n\n Pre-requisites: The tools for the course are setup on a virtu al machine that can be run locally via Vagrant/Virtualbox or remotely on a n EC2 instance. Students must have a laptop with 16GB of RAM to run the vi rtual machine for the tutorial\, otherwise an EC2 setup is available. User s wanting to setup PySpark locally on their machine will receive limited s upport\, so this is not\nfor beginners. \n\n Not interested in any of the tutorials? Bring your own project to hack on or bring your burning questi ons and we will try to hook you up with devs that can help you out. \n\n E xamples of projects to hack on: \n\n • Personal side projects - your web application or personal learning project \n\n • Open source projects - work on open issues or recruit developers for your project \n\n • Work p rojects - work on anything you like and bounce ideas around \n\n Hope to s ee you there! \n\n SF Python is run by volunteers aiming to foster the Pyt hon Community in the bay area. Please consider making a donation to SF Pyt hon and saying a big thank you to Linkedin for providing Pizza\, Beer\, Wi ne\, and the venue for this Wed's meetup. \n LAST-MODIFIED:20240728T211358 LOCATION:Linkedin\, 222 2nd Street\, San Francisco\, CA ORGANIZER:mailto:grace@pybay.com SUMMARY:Project Night at Linkedin URL;VALUE=URI:https://ti.to/sf-python/ProjectNightAtLinkedin URL;VALUE=URI:https://ti.to/sf-python/ProjectNightAtLinkedin END:VEVENT BEGIN:VEVENT DTSTAMP:20260602T083748Z UID:HvC3Aw1013229 DTSTART;TZID=America/Los_Angeles:20170215T180000 DTEND;TZID=America/Los_Angeles:20170215T213000 CLASS:PUBLIC CREATED:20170202T155708 LAST-MODIFIED:20171122T010242 LOCATION:Linkedin \, Linkedin\, 222 2nd Street\, San Francisco\, CA ORGANIZER:mailto:grace@pybay.com SUMMARY:[Project Night at Linkedin] SF Python Meetup URL;VALUE=URI:https://ti.to/sf-python/ProjectNightAtLinkedin URL;VALUE=URI:https://ti.to/sf-python/ProjectNightAtLinkedin END:VEVENT END:VCALENDAR