What we're about

This is a Meetup to explore the vendor, language and framework neutral practical applications of deep learning technologies.

Everybody with any level of expertise and background is welcome.

All you need is an interest in learning more about about deep learning and applying it to healthcare, biotech, medicine, finance, nanotechnology, artificial intelligence, robotics, neuroscience, information sciences, etc.

This will be a hands on practical group. We'll start out by working through Fast.ai's 7 week course "Practical Deep Learning For Coders, Part 1" http://course.fast.ai/index.html and then meet once a month (or so) to exchange ideas and talk about what we're working on, what we've learned, what we could use help with and current developments.

No worries if you're not totally 'caught up'. Come on out and discuss what you have been able to do.

We thought it'd be good to have a way to chat during the week as we worked on things so I've created a #deep-learning channel in the PDX Startups slack which you can join at https://pdx-startups-slack.herokuapp.com This should work better than the comments on the meetup page.

Looking forward to getting to know everybody and exploring deep learning.

Upcoming events (5+)

Vision based Human Pose Estimation w/Srujana Gattupalli

Articulated pose estimation is one of the fundamental challenges in computer vision. Progress in this area can immediately be applied to important vision tasks such as human tracking action recognition and video analysis. This talk will discuss papers and progress of computer vision and deep learning towards human pose estimation and its applications. Some papers that we can discuss here: Insafutdinov, E., Pishchulin, L., Andres, B., Andriluka, M., and Schiele, B. Deepercut: A deeper, stronger, and faster multi-person pose estimation model. CoRR abs/[masked] (2016). Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields, Zhe Cao and Tomas Simon and Shih-En Wei and Yaser Sheikh CVPR 2017 I will lead the discussion and would encourage everyone else to think about applications and future research/ development ideas in this domain. Bio: Srujana Gattupalli is a Deep Learning Software Engineer at Intel Corporation. She received a PhD degree in Computer Science from the University of Texas at Arlington in 2018. Her research interests are focused on Machine Learning, Computer Vision, Human-Computer interaction and their applications for human body motion estimation and pose tracking in assistive technology. Her academic work experience includes a role as a research assistant at the Vision Learning Mining lab and teaching assistant for graduate courses. She has been a Graduate Intern at Intel Corporation in 2017, working towards research and development for autonomous driving and machine learning algorithms. In addition to this, she has worked as a Software Engineer at Cerner Corporation in 2014. Ms. Gattupalli is an active member of Upsilon Pi Epsilon (UPE) honor society in computing. She has published 7 peer reviewed papers, received 2 international awards and has served as a reviewer in many others. In her spare time, she enjoys painting, philately, reading books, travel and to seek outdoor adventures.

Peer mentoring and deep learning discussions

Alchemy Code Lab

This is a placeholder for our regularly scheduled meetings on the first wednesday of the month.

Peer mentoring and deep learning discussions

Alchemy Code Lab

This is a placeholder for our regularly scheduled meetings on the first wednesday of the month.

Peer mentoring and deep learning discussions

Alchemy Code Lab

This is a placeholder for our regularly scheduled meetings on the first wednesday of the month.

Past events (35)

Machine Learning and JavaScript

Alchemy Code Lab