It's an antiquated and elitist viewpoint. There are no "online" and "offline" educations, nor "formal" and "informal". The best people are 90%+ self-educated, whether they have a degree from Stanford or not. The value-add of degrees in CS is increasingly marginal.
— François Chollet (@fchollet) December 28, 2018
Nice discussion about perceived qualifications for an ML role.
— hardmaru (@hardmaru) December 28, 2018
At another extreme, some think it’s a good idea to totally disregard schools, titles & degrees, but filter candidates based on H-index and 1st author publications at top ML conferences (for research roles). Thoughts? https://t.co/BXsho1MN3R
Confession...
— Andrew Trask (@iamtrask) December 28, 2018
... I think I'm online educated in Deep Learning...
- Arxiv/Blogs for new stuff - ONLY option!
- Books/MOOCs/Youtube for old stuff (3+ yrs) - BEST option IMO!
- Twitter for ML debates
...guess I'm unqualified 😉#100DaysOfMLCode https://t.co/n2PDqzMPkH
Also - I think the future of pedigrees are online - @udacity is leading the charge on this. There's no real reason education/pedigree should be limited to the number of people we can fit into a physical room.
— Andrew Trask (@iamtrask) December 28, 2018
It's just taking a while for hiring managers to realize this.
The short-sighted approach to mastering a topic is to dedicate all of your time and energy to study in depth what has been written about it so far.
— François Chollet (@fchollet) December 29, 2018
The long-sighted approach is to study a range of interconnected fields, and form your own mental models through relevant analogies.
I think a majority of people in my field spend too much time reading preprints, and not enough time reading books. All kinds of books.
— François Chollet (@fchollet) December 29, 2018
