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IMHO, the anxiety is well-placed. I did my first CSci homeworks on punchcards. FORTRAN was a teaching language in those days. The reason I am still relevant a

IMHO, the anxiety is well-placed. I did my first CSci homeworks on punchcards. FORTRAN was a teaching language in those days. The reason I am still relevant as a developer is that over the years I have learned to recognize progress when I see it. (Why am I the one dragging 30-year-old-luddites into the world of Python 3.x???? 2.7 is history. But I digress....) Back on point... this is one of those times in my career when I have to say "This changes everything." ML changes everything. How we formulate problems, how we architect solutions, how we program solutions. For practical advice: I started the Udacity self-driving car Nano-degree. (I won't be finishing it, but that is another story.) The first term is pretty much all ML. I found the Udacity course exactly what I needed to get a practical, hands-on intro and ramp-up to the current world of ML. I'm no expert, but I now at least recognize the vocabulary, have rubbed my brain cells against a few simple ML problems, and can move forward and learn more. Udacity also has a ML nano-degree. YMMV.

bm1362 9y ago
Why didn't you finish? I did the 3xxx self driving course on Udacity and was considering doing the nanodegree as well. Would really love to hear your perspective.
- cr0sh 9y ago
  If you can afford it, do the nanodegree. I did the CS373 course back in 2012 - and I have found that the nanodegree (which I am taking, and I am about 2/3s of the way thru the second term) touches on more than a bit of new stuff that I didn't find covered in the CS373 course (at least back in 2012). First term alone is worth the price, I think. Learning how to use OpenCV, TensorFlow and Keras, then using that knowledge to "drive" a virtual car (I built a version of the NVidia End-to-End CNN and trained it using CUDA and my GPU at home, using data I derived in the simulator by steering the virtual car using a steering wheel controller I bought just for that purpose). Second term so far has felt mostly like a re-hash of what was learned in the CS373 - although, learning about extended and unscented Kalman filters for integrating LIDAR and RADAR data was not something covered in CS373 (only the basic Kalman filter - useful in itself, though). My goal is to finish this puppy. From what I have heard (anecdotally), when the initial class started, for the first term there were approximately 30,000 students enrolled. When term 2 started, only about 300 of those students enrolled for the new term. So far, I'm doing pretty well with this. Considering I don't have an advanced degree, that I am a self-taught software developer, and I am going to have my 44th birthday in about a month - well, you know the story about dogs and tricks; that don't apply to me.
  - bm1362 9y ago
    Sounds good, thanks for the insight.
cr0sh 9y ago
> I started the Udacity self-driving car Nano-degree. (I won't be finishing it, but that is another story.) I'd be curious to know why myself? I'm about 2/3rds of the way thru the second term (I'm in the November cohort), and I plan to finish the nanodegree. What I have found, though, with this second term, is that it seems to be a rehash of the CS373 course (I took that back in 2012, when Udacity first started) - though it also has taught a few new concepts for me (I like the fact that it is mostly done in C++ - which had gotten really rusty for me - but now I am liking C++ more). I'd also recommend the Coursera ML course - from what I know, it is basically identical (if not the same material) as the original ML Class from 2011 (I took that one, which was my intro to MOOCs). If you can wrap your mind around the programming language used (Octave - basically an open-source clone of MatLab - where the main primitive is a vector/list), you'll find it does a great job at explaining many concepts that you might have had trouble with in the past (when we got to neural networks and backprop, it finally clicked for me - same with how think in terms to figure out how to parallelize processes).
- dbcurtis 9y ago
  OK, since a couple people are curious I'll spend the bandwidth. It really is an issue of personal circumstances, not any failing of the Udacity course. I like the video lectures a lot, and got a lot out of them. I like the quizes and short problems. The large projects are a little too color-by-numbers for my taste, I really would like to see an incremental exercise step in between in order to flesh out understanding more solidly, but if I had enough time I could probably fill than in myself. But as I said above, my decision not to follow through is mostly personal circumstances. When I signed up for the course I was casting around for something new to do and thought the course would add to my robotics knowledge and cred. By the time my class cohort started, I was working at a robotics startup. Aside from the startup taking a lot of time and energy, doing robots by day, robots at night, and robots on the weekend just got to be a bit much. Something in my life had to give, and Udacity didn't make the cut this time.
  - cr0sh 9y ago
    Hey - that's a perfectly reasonable answer, and I glad for you on the startup bit (sounds fun). You're right on the "color by numbers" part - but I think there's a couple of reasons for it. I think that the purpose of the course isn't geared only to ML and self-driving vehicle purposes, but also as a means toward general software development. So they don't expect everyone to be at the self-starting coder level. Secondly, if they were to, say, have you code each project from scratch with only a bare outline of what is needed, I think they'd have to likely expand the course (more lessons to teach things more in depth), as well as extend the time for the lessons; all of this would mean the course would be much longer, and that students would have to dedicate even more time to the effort, beyond what they already are doing. Some could do that, but I think that many wouldn't be able to; they have to strike a balance somewhere, and this has been their answer. Plus, I have a feeling that many of their students are currently students going to university in some manner, and not all of them in the United States, but abroad internationally. Many of these students are likely struggling just to make the payments for this nanodegree as-is (and may have contributed to the drop-out rate from term 1 to term 2). Making the course longer and more expensive might put it completely out of reach for those students.

bm1362 9y ago

Why didn't you finish? I did the 3xxx self driving course on Udacity and was considering doing the nanodegree as well. Would really love to hear your perspective.

cr0sh 9y ago
If you can afford it, do the nanodegree. I did the CS373 course back in 2012 - and I have found that the nanodegree (which I am taking, and I am about 2/3s of the way thru the second term) touches on more than a bit of new stuff that I didn't find covered in the CS373 course (at least back in 2012). First term alone is worth the price, I think. Learning how to use OpenCV, TensorFlow and Keras, then using that knowledge to "drive" a virtual car (I built a version of the NVidia End-to-End CNN and trained it using CUDA and my GPU at home, using data I derived in the simulator by steering the virtual car using a steering wheel controller I bought just for that purpose). Second term so far has felt mostly like a re-hash of what was learned in the CS373 - although, learning about extended and unscented Kalman filters for integrating LIDAR and RADAR data was not something covered in CS373 (only the basic Kalman filter - useful in itself, though). My goal is to finish this puppy. From what I have heard (anecdotally), when the initial class started, for the first term there were approximately 30,000 students enrolled. When term 2 started, only about 300 of those students enrolled for the new term. So far, I'm doing pretty well with this. Considering I don't have an advanced degree, that I am a self-taught software developer, and I am going to have my 44th birthday in about a month - well, you know the story about dogs and tricks; that don't apply to me.
- bm1362 9y ago
  Sounds good, thanks for the insight.

cr0sh 9y ago

> I started the Udacity self-driving car Nano-degree. (I won't be finishing it, but that is another story.) I'd be curious to know why myself? I'm about 2/3rds of the way thru the second term (I'm in the November cohort), and I plan to finish the nanodegree. What I have found, though, with this second term, is that it seems to be a rehash of the CS373 course (I took that back in 2012, when Udacity first started) - though it also has taught a few new concepts for me (I like the fact that it is mostly done in C++ - which had gotten really rusty for me - but now I am liking C++ more). I'd also recommend the Coursera ML course - from what I know, it is basically identical (if not the same material) as the original ML Class from 2011 (I took that one, which was my intro to MOOCs). If you can wrap your mind around the programming language used (Octave - basically an open-source clone of MatLab - where the main primitive is a vector/list), you'll find it does a great job at explaining many concepts that you might have had trouble with in the past (when we got to neural networks and backprop, it finally clicked for me - same with how think in terms to figure out how to parallelize processes).

dbcurtis 9y ago
OK, since a couple people are curious I'll spend the bandwidth. It really is an issue of personal circumstances, not any failing of the Udacity course. I like the video lectures a lot, and got a lot out of them. I like the quizes and short problems. The large projects are a little too color-by-numbers for my taste, I really would like to see an incremental exercise step in between in order to flesh out understanding more solidly, but if I had enough time I could probably fill than in myself. But as I said above, my decision not to follow through is mostly personal circumstances. When I signed up for the course I was casting around for something new to do and thought the course would add to my robotics knowledge and cred. By the time my class cohort started, I was working at a robotics startup. Aside from the startup taking a lot of time and energy, doing robots by day, robots at night, and robots on the weekend just got to be a bit much. Something in my life had to give, and Udacity didn't make the cut this time.
- cr0sh 9y ago
  Hey - that's a perfectly reasonable answer, and I glad for you on the startup bit (sounds fun). You're right on the "color by numbers" part - but I think there's a couple of reasons for it. I think that the purpose of the course isn't geared only to ML and self-driving vehicle purposes, but also as a means toward general software development. So they don't expect everyone to be at the self-starting coder level. Secondly, if they were to, say, have you code each project from scratch with only a bare outline of what is needed, I think they'd have to likely expand the course (more lessons to teach things more in depth), as well as extend the time for the lessons; all of this would mean the course would be much longer, and that students would have to dedicate even more time to the effort, beyond what they already are doing. Some could do that, but I think that many wouldn't be able to; they have to strike a balance somewhere, and this has been their answer. Plus, I have a feeling that many of their students are currently students going to university in some manner, and not all of them in the United States, but abroad internationally. Many of these students are likely struggling just to make the payments for this nanodegree as-is (and may have contributed to the drop-out rate from term 1 to term 2). Making the course longer and more expensive might put it completely out of reach for those students.