![]() ![]() 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.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.Preparation time: rotating hosts helps share preparation work.Between 2 courses, the forum (or the online service chosen as slack, etc.) could be used to discuss specific topics.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.Create a form to be filled in by each participant about the challenges: answers could help create small study groups.Logistic (classroom, communication channels, etc.).Too much preparation time for a single organizer.Temporal variability at which participants can attend.Too much to learn for the participants (DL theory and fastai library).Different levels of knowledge (beginner, intermediate, advanced).Empowerment of participants (weekly participation and stay until the end).– Quality survey completed by the participants? – How many posts from participants published? ![]() – Which percentage of participants stayed until the end? – How are the participants involved? (weekly presentations, etc.) ![]()
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