Lo que hacemos

Hands on meetups (lectures, classes, demo talks) with free compute time sponsored by Amazon AWS. We focus on giving members the opportunity to learn and share with the rest of the programming community.

Próximos eventos (5+)

Deep Learning Working Group

Tokyo

The goal of this working group is to cover papers starting with the mathematical concepts. This groups is project based where individuals can use the instructor to help implement projects. There are some beginner assignments but the goal is to not to try to cover material already covered by Coursera or Udacity in their deep learning programs. Led by Dr. Michal Fabinger from Tokyo University and founder of Tokyo Data Science. This group has a 1h lecture and discussion of individual projects. Agenda for week of 9/12 Covered last week: RAdam: https://arxiv.org/pdf/1908.03265.pdf GANs Wasserstein GAN:https://arxiv.org/abs/1701.07875 Earth Mover Distance for distance between probability distributions WGAN-GP:https://arxiv.org/abs/1704.00028 Spectral Normalization for GANS: https://arxiv.org/abs/1802.05957 This is live event hosted from Tokyo Japan 7pm PST Sun-Thu until 8/13. Zoom Conf Link: 7 pm PST https://zoom.us/j/169548352 There is an additional meetup meeting once a week on Tue 9pm PST focusing on computer vision. Current topics: 1) transpost convolutions: https://arxiv.org/pdf/1603.07285.pdf 2) unet: https://arxiv.org/abs/1505.04597 3) Improving unet w resnet-50 This is live event hosted from Tokyo Japan 9pm PST Tue until 8/13. Zoom Conf Link: 9pm pm PST EVERY TUE https://zoom.us/j/556248752

Apache Spark integration for Apache Iceberg. LIVE YouTube session

Holden Karau, Apache Spark Committer & PMC member, will show steps taken in exploring and discuss strategies in integrating a new project, Apache Iceberg into Apache Spark. This is a good opportunity for those interested in getting involved in open source. Ask questions and start with small PRs. Please subscribe to the youtube channel and click on the event reminder button so if the schedule changes you will be notified!! https://www.youtube.com/watch?v=axzknSKHzWY Iceberg: https://www.youtube.com/watch?v=nWwQMlrjhy0

Deep Learning Working Group

Tokyo

The goal of this working group is to cover papers starting with the mathematical concepts. This groups is project based where individuals can use the instructor to help implement projects. There are some beginner assignments but the goal is to not to try to cover material already covered by Coursera or Udacity in their deep learning programs. Led by Dr. Michal Fabinger from Tokyo University and founder of Tokyo Data Science. This group has a 1h lecture and discussion of individual projects. Agenda for week of 9/18 Covered last week: RAdam: https://arxiv.org/pdf/1908.03265.pdf GANs Wasserstein GAN:https://arxiv.org/abs/1701.07875 Earth Mover Distance for distance between probability distributions WGAN-GP:https://arxiv.org/abs/1704.00028 Spectral Normalization for GANS: https://arxiv.org/abs/1802.05957 This is live event hosted from Tokyo Japan 7pm PST Sun-Thu until 8/13. Zoom Conf Link: 7 pm PST https://zoom.us/j/169548352 Computer Vision: 2x week on Tue/Thu 9pm PST Current topics: 1) semantic segmentation 2) unet+resnet 50 3) spatial transformers:http://papers.nips.cc/paper/5854-spatial-transformer-networks.pdf This is live event hosted from Tokyo Japan 9pm PST Tue until 8/13. Zoom Conf Link: 9pm pm PST EVERY TUE https://zoom.us/j/55624875

Deep Learning Study Group

Hacker Dojo

Deep learning is evolving quickly. Important new developments are appearing daily. This group attempts to keep up by reading and discussing current deep learning literature. This meetup uses discussion among the participants to speed understanding of current research results. That requires that some participants read the paper before attending. Anyone is welcome to attend and listen without reading the paper. If nobody reads the paper the meeting will be short. Papers that we're reading, code that participants generate and other random stuff can be found at github site for the group. mike-bowles/hdDeepLearningStudy (https://github.com/mike-bowles/hdDeepLearningStudy) https://arxiv.org/pdf/1812.01729.pdf - Boltzman Generators - Sampling equilibrium states of many body systems with deep learning

Eventos anteriores (611)

Deep Learning Working Group

Tokyo