On course.fast.ai there is a good online course to refresh your knowledge about Deep Learning.

Topics offered are how backpropagation is implemented in PyTorch using inline gradients, collaborative filtering using embedded techniques, working of convolutional neural network layers, tabular analysis with random forests and gradient accumulation to reduce memory footprint of GPUs.

For many topics there is an example in Excel that really explains what is going on under the hood of a complex deep learning model.

Last lesson is about Data Ethics and explains how through feedback loops implemented in social media our society got more polarized in important discussions.

There is a book for the course, which we recommend as well:


Deep Space created the new website for the local boy scout society, APE Poschiavo. It also recovered existing content from the old website which was incorrectly migrated from the previous Internet provider. You can visit the new site here.

We built the kit of Prusa model i3 MK3s in about 12 hours, which is currently the best 3D printer available on the market.

The kit is ideal for learning how a 3D printer works. Josef Prusa modified the original Open Source RepRap design (Replicating Rapid Prototyper) where the plastic pieces of the 3D printer to be built are printed by another 3D printer. At time of writing Prusa Research has supplied 130'000 printers worldwide and owns a printing farm with 1'000 3D printers.

Gridcoin has just had a successful protocol update to version 5! Before version 5, it was mandatory for a cruncher to switch to the team Gridcoin, in order to be rewarded with this cryptocurrency.

From now on, any BOINC cruncher, regardless of team, can earn Gridcoin for their work. 
Solo cruncher must send a new beacon within the next 2 weeks to not lose any past unclaimed rewards.

The BOINC community is about 10 times the Gridcoin community. It is believed that the removal of the Team Requirement should spark interest among BOINC crunchers for Gridcoin.

As of this morning, the best resources of our Computational Cluster are dedicated to the projects Folding@home and Gene@home (through TN-Grid in BOINC), that directly look at how COVID19 interacts with the human ACE2 receptor. Rosetta@home and GPUGRID also partially look at COVID19 and there we already have a multi-year contribution. deep space computing also supports several other projects fighting common diseases with its CUDA Supercomputers. Our computational cluster was mainly built with recycled computers supercharged by graphic cards bought second hand.