These map to the learning objectives or learning goals. Levels
These map to the learning journey and as the learner goes through each level, it signifies a step up in proficiency for them. Instant feedback
This helps learners know how they are faring against their learning goals and based on this, they can adopt the necessary measures to step up their performance. Scores
They are indicators of their performance and are closely aligned to offering gratification as well as a sense of accomplishment. Badges
As the learners go through the learning path and clear certain levels, they are given badges. These reflect affirmations for their significant achievements. Leaderboards
They are dashboards that are used to provide a pictorial view of the overall progress—including against others. The analytics keeps learners connected to the learning journey and aligned to meeting their terminal objectives. Competition
This can be leveraged effectively as it helps learners assess where they stand against other peers or competing teams. Collaboration
This feature not only facilitates team-building but also enables learners to leverage the support of peers or guidance from experts to meet their goals. Points and currency Points are a variation on badges, and can also be used as currency within a program. For instance, you can offer learners a point for every comment the leave in the discussion forum, and then let them redeem a certain number of points for rewards and discounts. Progress bars and level up Progress bars can help by visualizing their advancements. You can even add a bit of interest by creating "level ups" that unlock new course features.
- Shiny Server Daniel Lakens: Recovering data from summary statistics, App to perform simulation based power analysis for ANOVA designs
Full Stack Deep Learning
CNN Sliced Inference
obss/sahi: A vision library for performing sliced inference on large images/small objects
Deep Learning for Forecasting
top open source deep learning for time series forecasting frameworks.
- Gluon This framework by Amazon remains one of the top DL based time series forecasting frameworks on GitHub. However, there are some down sides including lock-in to MXNet (a rather obscure architecture). The repository also doesn't seem to be quick at adding new research.
- Flow Forecast This is an upcoming PyTorch based deep learning for time series forecasting framework. The repository features a lot of recent models out of research conferences along with an easy to use deployment API. The repository is one of the few repos to have new models, coverage tests, and interpretability metrics.
- sktime dl This is another time series forecasting repository. Unfortunately it looks like particularly recent activity has diminished on it.
- PyTorch-TS Another framework, written in PyTorch, this repository focuses more on probabilistic models. The repository isn't that active (last commit was in November).
Deep Learning Noise Cancellation
Meshtastic An open source, off-grid, hiking, climbing, skiing, GPS mesh communicator. Similar to disaster.radio
2020-2021, Imron Rosyadi