Remote Practical Deep Learning for Coders Study Group Meets Sundays at 8AM PST, starting 12/01/2019

:sunny: Come Learn with our Remote Fastai Course 1v3 Study Group! :sunny:

The Practical Deep Learning for Coders study group meets on Sundays at 8:00 AM – 9:30 AM PST, beginning on December 01, 2019. Start time in other time zones: 11:00 AM EST, 5:00 PM CET, 9:30 PM IST

Join the meetup on Zoom at the above date & time

We’ll progress systematically through Lessons 1 through 7, covering the material in the videos and in the accompanying Jupyter notebooks

We’ll use this Forum thread for discussions related to the course materials, and for meeting announcements.

For other announcements, and administrative matters (such as polls), we’ll use the #fastai_dl1 channel on the TWiML & AI x fast.ai Slack Group, courtesy of Sam Charrington. To get an invitation to join the Slack Group, sign up here.

Meeting ID: 119 203 235

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Meeting ID: 119 203 235

Calendar Invite :
Time: Dec 1, 2019 08:00 AM Pacific Time (US and Canada)
Every week on Sun, until Jan 12, 2020, 7 occurrence(s).
Please download and import the Weekly iCalendar (.ics) files to your calendar system.

twimlai-x-fastai-3

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Looks like the info in the Slack link is out of date.

Looking into it. I don’t have the ability to edit that webpage.

Deep Learning is Not a Spectator Sport

In order to successfully learn the material of the Practical Deep Learning for Coders course, you should be working with the Jupyter course notebooks, in parallel with watching the videos and taking notes. It’s true that you can learn a lot from the videos, but if you want to be a practitioner, you should make it a goal to devote time to running, modifying, and playing with the notebooks.

So your first order of business is to establish the capability to run the fastai notebooks, either locally or on the Cloud, if you’ve not already done so. If you don’t have your own GPU there are many ways to access compute resources on the Cloud.

In my opinion, Google Colab is the most attractive Cloud option, as it is free, and offers access not only to GPUs, but also TPUs (Tensor Processing Units) as well!

I’ve created this Jupyter notebook to jump-start you on Colab.

I originally created the notebook for Fastai’s dl2 course. Follow the directions, except that wherever you see dl2 substitute dl1 to adapt it for this course.

Enjoy! Questions are welcome if you should run into problems.