What we're about


We organize and facilitate study groups, discussion, and classes to help each other discover and delve into deep learning and related topics.

All experience levels and professional backgrounds are welcome!


Disclaimer & Code of Conduct:

Deep Learning RTP Meetup is a fully open source, public forum. No expectation of privacy or secrecy regarding content or discussion either in person, in writing, or electronically should be assumed. Please refrain from including or sharing confidential material including, but not limited to, proprietary content, trade secrets, and classified and sensitive material.

Deep Learning Meetup is not liable for the content and/or discussion presented or shared in any form by individual members, visitors, or speakers. This disclaimer extends to all forms of Deep Learning Meetup’s events, discussion, and media- including, but not limited to, chat clients, digital work spaces and storage, and group discussion or collaborative projects.

DL RTP really likes the Recurse Center Rules (https://www.recurse.com/code-of-conduct). If a participant engages in harassing behavior, the organizers may take any action they deem appropriate, including warning the offender or expelling them from the class/event/meetup group.

Feel free to contact any of the DL Meetup Co-organizers with feedback or questions. Thank you for helping to ensure a healthy and friendly environment for all!


Weekly sessions:

Monday 5:30 - 8pm at Dulce Dessert Cafe, Fayetteville Rd, Durham

Thursday noon - 2pm at The Frontier in RTP

Sunday 10am - 1pm at HQ Raleigh


What you need to get started in Deep Learning (http://www.fast.ai/2017/11/16/what-you-need/)

Here's a link to Study Material (https://www.meetup.com/Deep-Learning-RTP/about/).

Work through classes and projects at your own pace at home. Come to a study session to ask and answer questions, spend quality time working on your code and presenting what you've learned. We are all over the place in terms of experience, but we make sure to keep the environment-friendly so that everyone's comfortable asking any question.

Recent history of deep learning from the fantastic podcast Talking Machines (http://www.thetalkingmachines.com/blog/):

• History of Deep Learning from the Inside Out - Part 1 (http://www.thetalkingmachines.com/blog/2015/2/26/the-history-of-machine-learning-from-the-inside-out) and Part 2 (http://www.thetalkingmachines.com/blog/2015/3/13/how-machine-learning-got-where-it-is-and-the-future-of-the-field)

Resources we've used in this study group:

• Start with Practical Deep Learning for Coders (http://course.fast.ai/) from fast.ai

• Networks and Deep Learning (http://neuralnetworksanddeeplearning.com/) from Michael Nielsen

• Machine Learning for Artists (https://ml4a.github.io/) by Gene Kogan et al

• Machine Learning with Scikit-Learn & Tensorflow (http://shop.oreilly.com/product/0636920052289.do) by Aurelien Geron

Courses worth looking into:

• Deep Natural Language Processing (https://github.com/oxford-cs-deepnlp-2017/lectures) from Oxford

• Convolutional Neural Networks for Visual Image Recognition (http://cs231n.stanford.edu/) from Stanford

• Numerical Linear Algebra (https://github.com/fastai/numerical-linear-algebra) from fast.ai

To Infinity and Beyond:

As the number of deep learning tutorials and lists of lists approaches infinity, here are some good places to browse -

• Awesome Deep Learning (https://github.com/ChristosChristofidis/awesome-deep-learning)

• Deep Learning Gallery (http://deeplearninggallery.com/)

• A Guide to Deep Learning (http://yerevann.com/a-guide-to-deep-learning/)

• and Arxiv Sanity Preserver (http://www.arxiv-sanity.com/)

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