37 karma · joined December 20, 2015
A book usually covers a selected topic in depth and in a coherent manner. This is particular useful for graduate students. Besides, some book authors also invite their friends to give comments. The impact of a book can be as rigorous and significant as journal articles.
If someone has tangible contributions through GitHub, they probably write that down in the resume. The interviewers may check their claims either during interview or on GitHub before/after the interview.
The problem is that if a candidate performs moderately well in the interview (e.g. coding interview), while he/she has made many contributions through GitHub, should the interviewer recommend the candidate? My belief is that most interviewers will not take the risk or take the responsibility for a potentially wrong hire.
How often will the clients find you again to do the follow-up work, e.g. new software feature requests, for the previous projects? If you refuse the requests, will this undermine the business relationship?
You're right that Torch is faster than TensorFlow in RNN. But Torch is slower than TesnorFlow in AlexNet and ResNet. There is a set of benchmarks for many DL approaches as found in https://github.com/soumith/convnet-benchmarks
It seems multi-national company may make money by providing a private room to enjoy the 'holoportation service'.
My feeling is that as long as the whole open system makes scientific progress, and suppresses malicious or false scientific results, it is fine.