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April 2, 2019 | Closing this topic and publication of the guide as markdown in a new topic : Fastai Study Group Guide
April 1, 2019 | End of the work on our Fastai Study Group Guide today. More information in this post. Thanks to all
March 12, 2019 | I posted the version 1 of the Fastai Study Group Guide at (link deleted).
By this post (October 2018), Jeremy facilitated the creation of local fastai Part 1 (2018) study groups. While Part 2 (2019) will start soon and groups continue to be created in the world using Part 1 (2018), it would be interesting to collect feedbacks from the organizers and participants of these groups in order to list the best practices and also to avoid some gaps.
First, we need to define the fields to qualify different aspects of a fastai study group, fields that will be used to create a Google sheet in which organizers as well as groups participants can enter their comments.
In order to start the discussion, I propose the list below. This list is not exhaustive. The goal is to improve it by exchanging posts on this thread.
In order to promote this discussion, I will post the following message in the threads of existing study groups of this forum to the organizers and groups participants.
Hello, I just started a discussion thread on fastai study groups to gather feedbacks from the organizers and participants in order to list the best practices and also to avoid some gaps.
You will find more information in this post. Thank you if you can take a few minutes to participate in the discussion.
Next week, I will summarize the proposals to define a list of feedback fields and post the link to the Google sheet.
Thank you all
List of feedback fields (draft):
- Challenges (1) / Recommendations (2)
- Collaborations (3)
- Follow up or professional projects (4)
- Selection of participants?
- Registration method (online registration form, etc.)?
- Number of organizers?
- Average number of participants?
- Number of meetings per week?
- Duration of a meeting?
- Classroom equipment? (projector, wifi, etc.)
- Site / Facebook page / twitter / Meetup for the study group?
- Cloud in order to store online all documents created in the sudy groups (slides, guides, notebooks, etc.)? (ex: a shared Google Drive with all reading/writing rights for all)
- Any (free) online working/communication channel out of the forum? (slack, telegram, whatsapp, Google Group, newsletter, etc.) (fastai forum + Slack seems to be a winning combination for most study groups)
- Definition of a course objective? (number of meetings, creation of a notebook, etc.)
- Ask volunteers to be referents in the installation of a DL environment?
- Before lesson 1, meeting on how to install a DL environment (python+numpy, GPU in laptop (Windows, Apple, Linux) / cloud, jupyter notebook)?
- Group participants by level (beginners and advanced) or create 2 groups (concepts and coding) and let participants choose?
- Creating groups of students to study outside of meetings?
- Proposing Kaggle challenges to groups?
- Teaching process? (an instructor presents the key points / no teaching but watch fastai videos / walking through the notebooks on a different dataset, sharing the learnings and discuss with the group how it can make it even better / other)
- Watch the fastai video in meetings?
- Coding session in meetings?
- Creation of slides by using @hiromi notes?
- Create a Participant Guide with installation advises, course content, list of resources and how to study at home (see How to do fastai - Study plans & Learning strategies)?
- Get some parameters/numbers/quality evaluation to estimate the success of a fastai study group (evolution of number of participants, number of presentations by participants, number of blog posts, number of published notebooks, satisfaction survey, etc.)
– How are the participants involved? (weekly presentations, etc.)
– Which percentage of participants stayed until the end?
– How many posts from participants published?
– How many projects launched?
– How many notebooks published?
– Quality survey completed by the participants?
- Other problems
- Empowerment of participants (weekly participation and stay until the end)
- Different levels of knowledge (beginner, intermediate, advanced)
- Different personal goals
- Different learning speeds
- Too much to learn for the participants (DL theory and fastai library)
- Temporal variability at which participants can attend
- Too much preparation time for a single organizer
- Logistic (classroom, communication channels, etc.)
- Stay on schedule
- Create a form to be filled in by each participant about the challenges: answers could help create small study groups.
- Each course should have a lesson review (of the precedent lesson as a reminder and at the end in order to fix new contents in mind) and a practical exercise.
- Between 2 courses, the forum (or the online service chosen as slack, etc.) could be used to discuss specific topics.
- Preparation time: rotating hosts helps share preparation work
- Initiatives of diverse topics: mini-presentations of 10-30 minutes are welcomed (it encourages more participation and offer a refresher of the course details or projects fastai-based), winning solutions of long-term hackathons, student projects, implementing papers, transitional ML techniques, explainable AI etc to mix with fastai lessons to meet our diverse audiences’ needs.
- Different kind of meetings at different time (evening, lunch, etc.): there is the main weekly meeting but offering other slots for presentations for example can be a solution to reach out those who can’t attend the main meetup.
- Keep learning with fun : do a group project together to learn and have fun and after evening meetup, go out for dinner to continue nerd-chat.
- Growing of the study group: get one or more local sponsor in order to help with venue larger, food and drinks, and even cloud computing credits.
- with local companies and if yes, how?
- with local universities or schools and if yes, how?
- with initiatives like City AI, AI saturdays, meetups or others and what would be the benefit?
- Be part of a bigger communities in your local area (for example, do presentations in local research institute and universities)
(4) Follow up or professional projects
- Did you try to organize special interest groups around topics (eg to deoldify pictures, analyze tabular data, language topics) to deep dive and prototype apps?
- Weekly coworking sessions ?