4 ms·

I just enrolled in the Udacity SDC nanodegree. Can you elaborate?

bitL 10y ago
It's pretty cool, you'll have fun! It's "practical" not "theoretical", so the focus is on getting code working and understanding bare minimum of math you need.
- kyle_martin1 10y ago
  Sounds pretty similar to Udacity's AI for Robots course! Good to know that they focus on intuition rather than rigor. Many professor enjoy stroking their ego with rigorous math, rather than communicating higher-level ideas and making their subject seem "easy".
  - bitL 10y ago
    AI for Robotics is a way way simpler course than SDC ;-) In SDC you actually get to train CNN to classify traffic signs from the scratch, to make a car in a racing game learn to drive similarly to your style and to detect real-world road features properly as you are driving. It made me smile very often when I saw how simple they made it appear :)
    - kyle_martin1 10y ago
      Of course haha! I was remarking on the breadth by which they explore and prove the underlying math.
  - windsignaling 10y ago
    That perspective seems a little naive. They are professionals, so they know "what it takes" to be good at the subject. Machine learning math is not rigorous in comparison to the type of math you'd study as a math or physics major. Machine learning math is useful precisely because of the fact that it shows you intuitively how the concepts work. Knowing the "high level" ideas is near-useless. I can get the "high level" ideas of quantum mechanics from any high school or pop science physics book. Does that mean I know quantum mechanics? Only at an extremely superficial level. And I suspect anyone taking a MOOC on AI or machine learning wants to end up with more than superficial knowledge.
    - kyle_martin1 10y ago
      Sorry I wasn't clear about "high level". I was mostly speaking about math professors in college. I have degrees in Math and EE...advance math isn't an issue. Having abstract intuition (aka "high level ideas") in math is incredibly value. Sometimes things get hairy in the computation and you need to step back and consider the bigger picture again. If you don't understand how the math works then you don't have the intuition and vice-vera.
cr0sh 10y ago
@bitL's comment of it being "practical, not theoretical" is pretty spot-on. I'm currently enrolled in and taking the course; I've also taken Udacity's CS373 course (when it was first offered in 2012) - and early courses that formed the foundation of Udacity and Coursera (ie - the Stanford-sponsored AI Class and ML Class, respectively). Again - what I noted in another comment - if you aren't comfortable with coding (Python and C/C++ in the nanodegree course), or linear algebra - you might have a tough time. You'll also likely want to brush-up on your stats/probability knowledge (though this hasn't come up yet - not like the CS373 course, or the AI and ML Classes). Finally, while you won't absolutely (well - not that I've seen yet) need super-detailed knowledge of what a derivative is or how it is derived (?) - that bit of Calculus seems to be important in machine learning (it is something I struggle with). Ultimately, give it a shot and have some fun; so far, I have enjoyed everything (just finished the lesson and lab for the Keras framework last night). Don't get too hung-up on the "due dates" for projects; ultimately the main due-date is the end of the term (you have to have everything completed by then to not have to retake the term). Be sure to use the forums, your mentor, the Slack channels, and the facebook page to get help and inspiration. Oh - and you are able to publish your solutions and code around, but if you decide or need to use code provided by someone else (or you use someone else's solution, or ideas from a paper, etc) - YOU NEED TO DOCUMENT THIS in the project rubric (otherwise it will be considered plagiarism which, if found out, may get you kicked from the course) - so be sure to credit your sources! Finally - if you enjoy the course - be sure to check out Udacity's CS373 course, their "Intro to Artificial Intelligence" course (both of those I believe are free?), and Coursera's Machine Learning course (also free). There are also a ton of other great online resources and books out there; let me know if you need or want suggestions!
deepnotderp 10y ago
On mobile so this won't be thorough, but compare the udacity course to the free (!) Coursera course from Andrew ng and you'll see the difference. I'm not even talking about mathematical rigor, I myself prioritize intuition over symbolic rigor, but you need to have the intuition of what's going on, not treat keras like a black box that magically spits out a ConvNet. Another thing I dislike is the lack of real world production deployment work. In an engineering oriented course, this should be the top focus. Unfortunately, this isn't really the case. That being said, I would say it's better than many universities.

bitL 10y ago

It's pretty cool, you'll have fun! It's "practical" not "theoretical", so the focus is on getting code working and understanding bare minimum of math you need.

kyle_martin1 10y ago
Sounds pretty similar to Udacity's AI for Robots course! Good to know that they focus on intuition rather than rigor. Many professor enjoy stroking their ego with rigorous math, rather than communicating higher-level ideas and making their subject seem "easy".
- bitL 10y ago
  AI for Robotics is a way way simpler course than SDC ;-) In SDC you actually get to train CNN to classify traffic signs from the scratch, to make a car in a racing game learn to drive similarly to your style and to detect real-world road features properly as you are driving. It made me smile very often when I saw how simple they made it appear :)
  - kyle_martin1 10y ago
    Of course haha! I was remarking on the breadth by which they explore and prove the underlying math.
- windsignaling 10y ago
  That perspective seems a little naive. They are professionals, so they know "what it takes" to be good at the subject. Machine learning math is not rigorous in comparison to the type of math you'd study as a math or physics major. Machine learning math is useful precisely because of the fact that it shows you intuitively how the concepts work. Knowing the "high level" ideas is near-useless. I can get the "high level" ideas of quantum mechanics from any high school or pop science physics book. Does that mean I know quantum mechanics? Only at an extremely superficial level. And I suspect anyone taking a MOOC on AI or machine learning wants to end up with more than superficial knowledge.
  - kyle_martin1 10y ago
    Sorry I wasn't clear about "high level". I was mostly speaking about math professors in college. I have degrees in Math and EE...advance math isn't an issue. Having abstract intuition (aka "high level ideas") in math is incredibly value. Sometimes things get hairy in the computation and you need to step back and consider the bigger picture again. If you don't understand how the math works then you don't have the intuition and vice-vera.

cr0sh 10y ago

@bitL's comment of it being "practical, not theoretical" is pretty spot-on. I'm currently enrolled in and taking the course; I've also taken Udacity's CS373 course (when it was first offered in 2012) - and early courses that formed the foundation of Udacity and Coursera (ie - the Stanford-sponsored AI Class and ML Class, respectively). Again - what I noted in another comment - if you aren't comfortable with coding (Python and C/C++ in the nanodegree course), or linear algebra - you might have a tough time. You'll also likely want to brush-up on your stats/probability knowledge (though this hasn't come up yet - not like the CS373 course, or the AI and ML Classes). Finally, while you won't absolutely (well - not that I've seen yet) need super-detailed knowledge of what a derivative is or how it is derived (?) - that bit of Calculus seems to be important in machine learning (it is something I struggle with). Ultimately, give it a shot and have some fun; so far, I have enjoyed everything (just finished the lesson and lab for the Keras framework last night). Don't get too hung-up on the "due dates" for projects; ultimately the main due-date is the end of the term (you have to have everything completed by then to not have to retake the term). Be sure to use the forums, your mentor, the Slack channels, and the facebook page to get help and inspiration. Oh - and you are able to publish your solutions and code around, but if you decide or need to use code provided by someone else (or you use someone else's solution, or ideas from a paper, etc) - YOU NEED TO DOCUMENT THIS in the project rubric (otherwise it will be considered plagiarism which, if found out, may get you kicked from the course) - so be sure to credit your sources! Finally - if you enjoy the course - be sure to check out Udacity's CS373 course, their "Intro to Artificial Intelligence" course (both of those I believe are free?), and Coursera's Machine Learning course (also free). There are also a ton of other great online resources and books out there; let me know if you need or want suggestions!

deepnotderp 10y ago

On mobile so this won't be thorough, but compare the udacity course to the free (!) Coursera course from Andrew ng and you'll see the difference. I'm not even talking about mathematical rigor, I myself prioritize intuition over symbolic rigor, but you need to have the intuition of what's going on, not treat keras like a black box that magically spits out a ConvNet. Another thing I dislike is the lack of real world production deployment work. In an engineering oriented course, this should be the top focus. Unfortunately, this isn't really the case. That being said, I would say it's better than many universities.