I'm surprised there was no mention (yet!) to fast.ai here. I've decided to learn deep learning this year, after many failed tries with other approaches. Their framework (built over PyTorch), their course, and their community around it are simply the best I've found so far. Very much recommend to anyone who knows a bit of coding and wants to learn Deep Learning quickly and pragmatically.

Can you describe a bit more on why you’ve found this more helpful? Any cool hobby projects you’ve been able to build from this or other applications?

Thanks for the shoutout @zetalabs. FWIW, the whole book is also available for free here https://impactscience.dev/

If you are looking for an alternative, I'd recommend checking out Eversend. (Disclosure, my GF is a co-founder). Much smaller company, but trying to grow it right (malmo FastTrack, Berlin tech starts, ...

OpenCollective [1] is a possible solution. It provides a way to support (monthly contributions or dedicated campaigns), but also the whole back-end of tracking the money and spending, and at least some degree of legal support with paperwork, invoices, ...

1.- https://opencollective.com/

I'm surprised there's no mention to adaptive optics in solar physics. It's essentially the same, but some interesting differences. Since there's more light you can have more corrections per second, better approaching the assumption of constant deformación during between corrections on a small view angle ("constant isoplanatic patch"). Also there's no "perfect star" to correct to, so calculation run on a closed loop to basically improve contrast of the reference. The needed correction is also bigger during the day, since the atmosphere is more turbulent. In the last few years there also have been really cool improvements to account for different layers of the atmosphere and better faster algorithms to correct wider and wider fields of view. This is an example (from my PhD) of the state of the art 9 years ago, but illustrates the huge difference: https://youtu.be/x3JkjXco6m0

That plane belong to the basemap, months/years old. NOT MH370. The map (zoomed out) shows fresh footprints. Fresh data here: https://www.mapbox.com/labs/blackbridge/flight-mh370/

[Source: We host those maps. ]

Why not just weighting the value of each like by the number of likes given over a period of time? People who spare the likes will give more value, those who like it all, won´t really add much value with their like.

This may well be a direction I take it in future once I have more data to make a more informed decision :)

Wouldn´t the sun-synchronous orbit allow you to use a similar a pipeline with local-time filtering to create a night-time map? That would be quite amazing, and also useful for socio-economic folks (or even correlate that with OSM coverage)

I could only find a related talk about this here: http://modis.gsfc.nasa.gov/sci_team/meetings/199905/presenta...

Instead of following the Amazon link, you can go to O'reilly page and use the code CFSTNY to get 50% off. (almost no difference with Amazon.com Kindle version, but you also get the pdf)

http://shop.oreilly.com/product/0636920023784.do?code=CFSTNY

"You did not meet the criteria for this discount" Know what the reason could be?

expired at 12pm PDT (3pm EDT)?

or you tried to get anything other than pure PDF (discount does not apply to dead tree format)

Thanks, I just picked up a copy.

Longer thrust would not be equivalent to bigger thrusts for non vertical paths, neither the arrival time or vectors once there. That's probably related.

I can also imagine that even the weight profile will change, thus leading to different paths...