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This is so dumb. You arent going to understand or get a job in machine learning unless you have at least a masters in the subject. Its extremely difficult and c

This is so dumb. You arent going to understand or get a job in machine learning unless you have at least a masters in the subject. Its extremely difficult and complex. I see tons of college students taking machine learning classes in anticipation of becoming a dinosaur amd none of them could get a machine learning job afterwards. Programming has always been pretty easy. The ai revolution wont be like the home computer revolution. Its going to be led by a relatively small group of academics, scientists and engineers working in prestigious research positions.

cr0sh 10y ago
Currently there just aren't that many ML jobs out there to apply for - but who knows what the landscape will be like in 5 years or so? Your assertion, though: > This is so dumb. You arent going to understand or get a job in machine learning unless you have at least a masters in the subject. Its extremely difficult and complex. ...couldn't be further from the truth. You can understand this stuff without a masters in the subject. It really isn't too difficult or complex. Sure, I will admit that understanding how to take a derivative might be useful, but despite not having that knowledge (but I'm working on obtaining it), I have still been able to implement successfully working ML solutions - at least in a classroom-type environment. My last success was getting a virtual car to drive around a virtual track, staying on the track and negotiating the curves, using an implementation of the NVIDIA End-to-End CNN architecture and some data I generated (plus augmentation and some other fun stuff). I used Keras and Python 3, running on my workstation at home, with a 750 ti SC as my "GPU" (I really need to upgrade this). My model converged very well after 10 epochs, but after 20 the loss was pushed pretty low to sub 1%. As far as I could tell, there wasn't evidence of overfitting (I need to do more investigation on this, though). This was all done as part of Udacity's Self-Driving Car Engineer Nanodegree, which I am taking part in. Prior to this, I also completed Udacity's CS373 course in 2012, and Andrew Ng's ML Class in 2011. My motivation for all of this has mainly been my interest in autonomous unmanned ground vehicle robotics technology. I have an ongoing side-project in developing such a platform (seemingly back-burner'd a lot, though - life getting in the way, I guess). Even so, if a job offer comes about because of it, I'm not going to complain. As it is, I believe the knowledge has helped me land positions, since it shows my dedication to improving my skills in problem domains outside of the everyday software development tasks. When potential employers have asked about it, I can show them some code I've worked on, while mentioning how some of the more simpler ML methods could help in a business problem domain. It sets me apart somewhat from other candidates, I believe. Especially those who think the topic isn't worth their time to learn, because it may be "difficult and complex".

cr0sh 10y ago

Currently there just aren't that many ML jobs out there to apply for - but who knows what the landscape will be like in 5 years or so? Your assertion, though: > This is so dumb. You arent going to understand or get a job in machine learning unless you have at least a masters in the subject. Its extremely difficult and complex. ...couldn't be further from the truth. You can understand this stuff without a masters in the subject. It really isn't too difficult or complex. Sure, I will admit that understanding how to take a derivative might be useful, but despite not having that knowledge (but I'm working on obtaining it), I have still been able to implement successfully working ML solutions - at least in a classroom-type environment. My last success was getting a virtual car to drive around a virtual track, staying on the track and negotiating the curves, using an implementation of the NVIDIA End-to-End CNN architecture and some data I generated (plus augmentation and some other fun stuff). I used Keras and Python 3, running on my workstation at home, with a 750 ti SC as my "GPU" (I really need to upgrade this). My model converged very well after 10 epochs, but after 20 the loss was pushed pretty low to sub 1%. As far as I could tell, there wasn't evidence of overfitting (I need to do more investigation on this, though). This was all done as part of Udacity's Self-Driving Car Engineer Nanodegree, which I am taking part in. Prior to this, I also completed Udacity's CS373 course in 2012, and Andrew Ng's ML Class in 2011. My motivation for all of this has mainly been my interest in autonomous unmanned ground vehicle robotics technology. I have an ongoing side-project in developing such a platform (seemingly back-burner'd a lot, though - life getting in the way, I guess). Even so, if a job offer comes about because of it, I'm not going to complain. As it is, I believe the knowledge has helped me land positions, since it shows my dedication to improving my skills in problem domains outside of the everyday software development tasks. When potential employers have asked about it, I can show them some code I've worked on, while mentioning how some of the more simpler ML methods could help in a business problem domain. It sets me apart somewhat from other candidates, I believe. Especially those who think the topic isn't worth their time to learn, because it may be "difficult and complex".