PyTorch With Baby Steps: From y = x To Training a ConvNet

May 8, 2018 · Austin, United States of America

This month Josh Mitchell will walk us through an explanation of PyTorch. The talk should be a great one for the newcomer and novice alike.

Description:

In this talk, Josh is going to take as anti-kitchen sink of an approach as possible to explaining PyTorch. In other words, he is going to start with demonstrating how to build a really obvious, really stupid model, then he's going to add one tiny piece to it at a time until it's a basic Convolutional Neural Network. His (lofty) goal is to make a large amount of jumps in such a way that he doesn't lose anyone, but he'll move fast enough that he won’t bore anyone.

After Josh's talk, we'll head over to CU29 (across the street from the Omni) for networking and deep learning discussions.

Parking is available at the Omni for a discounted rate of $5 for meetup attendees. Also, metered parking is free in downtown after 6pm on Tuesdays.

Agenda:

6:30 - 7:00: Sign In/Grab some food/Announcements
7:00 - 8:00: Josh's Talk
8:00: Head on over to CU29

About our host:

Capital Factory (https://capitalfactory.com/) is the entrepreneurial center of gravity in Austin, Texas. Located in the middle of downtown, Capital Factory has more than 60,000 square feet full of startups and entrepreneurs. Take classes to learn the skills that startups need, attend meetups to find a co-founder, rent a desk for your startup or apply for funding and mentorship in the incubator and accelerator.

Event organizers
  • Austin Deep Learning

    Welcome to the official Deep Learning meetup in Austin, Texas. Deep learning offers exciting solutions to an array of computer vision, natural language processing, and other data science problems, made possible by faster computing, richer data sets, and open source frameworks. We invite talks from machine learners and data scientists applying deep learning to solve problems along with tutorials and lessons learned. Talks are open to all Deep Learning frameworks (e.g., TensorFlow, Keras, PyTorch, etc.) Ple

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