On Feb 14, join ~150 devs at SF Python's presentation night and learn more about data. Our generous sponsor Yelp will also provide pizza and beer for the evening.
Please register via our ticketing partner Tito: (https://ti.to/sf-python/let-s-make-sense-of-data-on-valentine-s-day)
If you'd like to give a 5 mins lightning talk or 10-15 mins short talk at this or upcoming meetups, please submit your talk idea here (https://goo.gl/forms/ICpqIMunLo3ZgsrC3).
Lightning talks (5 mins)
Adopting PyTest by Darshan Ahluwalia
Poochr: how to recommend dog breeds using deep learning by Aaron Wiegel
Takes Two to Data Clean by Raul Maldonado
Main talk (~30 mins + Q&A)
TALK #1 - Understanding A/B test analysis by simulating data
A/B tests are at the heart of every data driven company, but how well do we understand the statistical tests available to us, especially when the data are far from textbook examples? Experimentation is a great way to learn and get an intuitive feel for the math underlying A/B test analysis. By simulating data and turning a few knobs, we can observe how test analysis results behave. Topics covered:
- Sampling from random distributions using numpy and scipy.
- Parametric and non-parametric tests in scipy.
- Efficient randomization tests in Python.
- Visualization with matplotlib, Bokeh, and Holoviews.
- Interactivity with Jupyter notebooks and ipywidgets.
Dennis O'Brien is Director of Data Science at GSN Games where he creates models, builds pipelines, wrangles data, runs experiments, and extracts insights from data. He has been in the video game industry for almost 20 years and has used Python in every job along the way.
TALK #2 - Deep Learning with PyTorch
1. What's Deep Learning and how is it different from Machine Learning
2. What's PyTorch and why are people excited about it.
3. What are the important differences between PyTorch and other frameworks like Tensorflow / Keras.
4. Walk through a few examples to highlight the API and design choices that sets PyTorch apart.
Ramesh Sampath is an Machine Learning Engineer with a background in software engineering. He loves to build Machine Learning models and deploy them as API services. Although he's been working on Deep Learning models recently using PyTorch and TensorFlow, he has special affinity for Numpy, Pandas and Scikit-Learn. He co-wrote a python ML Library called ML-Insights to introspect Scikit-Learn Models (http://ml-insights.readthedocs.io/).
6:00p - Check-in and mingle, with food provided by our generous sponsor Yelp!
7:05p - Welcome
7:10p - Announcements, lightning talks, and main talk
7:30p - Doors Close
8:20p - More mingling
9:30p - Hard Stop
**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 Yelp for providing pizza, beer and the venue for this Wed's meetup.
**Yelp sees 89 million mobile users and 79 million desktop users every month. Keeping everything running smoothly requires the best and brightest in the industry. Their engineers come from diverse technical backgrounds and value digital craftsmanship, open-source, and creative problem-solving. They write tests, review code, and push multiple times a day. Come out and talk to them.
140 New Montgomery