Next Meetup

[Two Series Meetup - Oct 10th and 25th] Deep Learning 101 by Andrew Draganov
We are excited to get started on our Deep Learning journey! -------------------------------------------- Meetup Agenda -------------------------------------------- 6:00 - 6:30 pm : Meet and Greet (with Pizzas for dinner) 6:30 - 6:45 pm : Meetup Announcements 6:45 - 8:00 pm : Talk by Andrew 8:00 - 8:30 pm : Q&A -------------------------------------------- Presentation of the Day -------------------------------------------- Our meetup member Andrew Draganov has kindly volunteered to share an introductory course he has taught before. He had taught this material as a 1-credit course at UVA a couple years ago. He will be expanding this material with some of the latest know-how. Thank you, Andrew! Link to the original course: Day 1 [Oct 10th] - Intro to data and machine learning - What problems are we trying to solve? - Classification, regression, clustering, etc. - Why are we trying to solve them? - Example applications for each - How does machine learning approach these problems - Learning from data & error minimization - Labeled data vs. unlabeled data - Supervised learning vs. unsupervised learning - Supervised learning is like flash cards - Unsupervised learning is like playing soccer in a team - Train vs. test set - What makes data good? - "Data is a high dimensional representation of information" - Data diversity - Covering the whole space of options - Even distribution across data space - Old machine learning models (with applications and code locations) - Supervised - regression, svm, bayesian/probabilistic, nearest neighbor - Unsupervised - K-means, PCA - Libraries for using them - New machine learning models - Look ahead to next time - Discussion Day 2 [Oct 25th] - Intro to neural networks and deep learning - Review of last time - Problems of old machine learning models - How are neural networks better? - How are neural networks worse? - Where are neural networks being used - Translation, image captioning, detection, etc etc etc. - Some stunning results - Celebrity GANs, self-driving cars, game playing - How a neural network goes from input to output - Feed-forward weights and activations - Backpropagation - Variations on the classic fully connected network - Convolutions, RNNs, GANs - With applications! - Code libraries - Code examples - Feedback opportunities - Discussion This will be a great mix of introductory and latest information of the Deep Learning field. Don't miss it! See you there. -------------------------------------------- Presenter Bio: -------------------------------------------- Andrew graduated from UVA in 2017 with a Math/CS double major and have been working hands-on developing deep learning algorithms at Expedition Technologies since. He specifically loves the intersection of probability theory and deep learning, and is always trying to learn more. He taught a class at UVA called Neural Networks in Application, and this is an extension of that effort in some respects. On his own time, he likes to snowboard, cook and play guitar. -------------------------------------------- Call for sponsors and speakers: -------------------------------------------- Please let Bill or Pragyan know if you would like to learn more about sponsoring and/or speaking at our meetup. Note: We would love to hear from you - even if you have an idea for a short 10 min /15 min talk.

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What we're about

NOVA Deep Learning is a group for anyone interested in meeting to discuss the concepts and technologies associated with Deep Learning. It will be a collaborative learning forum where everyone aids each other’s growth in this very specific domain. We will also have the “regular” meetup every quarter at a minimum (to begin with) – that is, with speakers who are experts in the Deep Learning space and have interesting stories to share. In addition, we will hold additional meetups around the learning of Deep Learning.

All skill levels are welcome and encouraged to participate and influence the direction of the group.

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