[cr0sh]

This is pretty cool; as someone who is currently working on the second project (traffic sign recognition) for the Udacity "Self-Driving Car Engineer" nanodegree, using TensorFlow - it is interesting to me how it seems like the "standard" MNIST CNN can be adapted to so many other use cases.

For the project I am currently working on, I'm using a slightly modified form of LeNet - which isn't too different from the TF MNIST tutorial; after all, recognizing traffic signs isn't much different than recognizing hand-written numbers...

...but "driving" a course? That seems radically different to my less-than-expert-at-TensorFlow understanding, but that is only due to my ignorance.

I'm glad that these examples and demos are being investigated and made public for others - especially people learning like myself - to look at and learn from.

[halflings]

From the post:

> Later, I switched to use Nvidia’s Autopilot...

So I guess he didn't use the MNIST CNN model.

[cr0sh]

However, if you look at the code:

https://github.com/SullyChen/Autopilot-TensorFlow/blob/maste...

You can see that it follows much the same pattern as LeNet CNN for MNIST - a few (ok, more than a few!) convolutional layers followed by a few fully connected layers.

Maybe you could call it a "follow on" or perhaps an ANN pattern?:

Conv -> Conv -> Reshape/Flatten -> FC -> FC -> FC

(disregarding activation and such)

...which is really the lesson of the LeNet MNIST CNN - at least, that's my takeaway.

[halflings]

You're right, that does look similar... I expected this to be based on some type of RNN!

[glidek]

As someone who's interested in taking the Udacity course, would your recommend it? Do you think the course prepares you enough find a Self-Driving developer job? Would you learn enough to compete/work along side people who got their Masters/PhD in Machine Learning? Appreciate your input.

[cr0sh]

> As someone who's interested in taking the Udacity course, would your recommend it?

So far, yes - but that has a few caveats:

See - I have some background prior to this, and I think it biases me a bit. First, I was one of the cohort that took the Stanford-sponsored ML Class (Andrew Ng) and AI Class (Thrun/Norvig), in 2011. While I wasn't able to complete the AI Class (due to personal reasons), I did complete the ML Class.

Both of these courses are now offered by Udacity (AI Class) and Coursera (ML Class):

https://www.udacity.com/course/intro-to-artificial-intellige...

https://www.coursera.org/learn/machine-learning

If you have never done any of this before, I encourage you to look into these courses first. IIRC, they are both free and self-paced online. I honestly found the ML Class to be easier than the AI class when I took them - but that was before the founding of these two MOOC-focused companies, so the content may have changed or been made more understandable since then.

In fact, now that I think about it, I might try taking those courses again myself as a refresher!

After that (and kicking myself for dropping out of the AI Class - but I didn't have a real choice there at the time), in 2012 Udacity started, and because of (reasons...) they couldn't offer the AI Class as a course (while for some reason, Coursera could offer the ML Class - there must have been licensing issues or something) - so instead, they offered their CS373 course in 2012 (at the time, titled "How to Build Your Own Self-Driving Vehicle" or something like that - quite a lofty title):

https://www.udacity.com/course/artificial-intelligence-for-r...

I jumped at it - and completed it as well; I found it to be a great course, and while difficult, it was very enlightening on several fronts (for the first time, it clearly explained to me exactly how a Kalman filter and PID worked!).

So - I have that background, plus everything else I have read before then or since (AI/ML has been a side interest of mine since I was a child - I'm 43 now).

My suggestion if you are just starting would be to take the courses in roughly this order - and only after you are fairly comfortable with both linear algebra concepts (mainly vectors/matrices math - dot product and the like) and stats/probabilities. To a certain extent (and I have found this out with this current Udacity course), having a knowledge of some basic calculus concepts (derivatives mainly) will be of help - but so far, despite that minor handicap, I've been ok without that greater knowledge - but I do intend to learn it:

1. Coursera ML Class 2. Udacity AI Class 3. Udacity CS373 course 4. Udacity Self-Driving Car Engineer Nanodegree

> Do you think the course prepares you enough find a Self-Driving developer job?

I honestly think it will - but I also have over 25 years under my belt as a professional software developer/engineer. Ultimately, it - along with the other courses I took - will (and have) help me in having other tools and ideas to bring to bear on problems. Also - realize that this knowledge can apply to multiple domains - not just vehicles. Marketing, robotics, design - heck, you name it - all will need or do currently need people who understand machine learning techniques.

> Would you learn enough to compete/work along side people who got their Masters/PhD in Machine Learning?

I believe you could, depending on your prior background. That said, don't think that these courses could ever substitute for graduate degree in ML - but I do think they could be a great stepping stone. I am actually planning on looking into getting my BA then Masters (hopefully) in Comp Sci after completing this course. Its something I should have done long ago, but better late than never, I guess! All I currently have is an associates from a tech school (worth almost nothing), and my high school diploma - but that, plus my willingness to constantly learn and stay ahead in my skills has never let me down career-wise! So I think having this ML experience will ultimately be a plus.

Worst-case scenario: I can use what I have learned in the development of a homebrew UGV (unmanned ground vehicle) I've been working at on and off for the past few years (mostly "off" - lol).

> Appreciate your input.

No problem, I hope my thoughts help - if you have other questions, PM me...

[melvinmt]

> the Udacity "Self-Driving Car Engineer" nanodegree

That looks like a great course by the way, thanks for sharing.

[jrheard]

I'm one of cr0sh's classmates. I don't have any background in ML/AI/etc, so I've had to supplement the Udacity course materials with a lot of external resources (just finished watching the Stanford CS231n course, which was very helpful), but overall the course been really interesting+fun so far. It's really nice to be exposed to new kinds of tech I've never heard of / used before. Refreshing change from webdev.

If you're strapped for cash and don't want to pay the $800/term, you could definitely learn these things on your own using free online resources. If you don't mind the price, though, I've found this course worth the time+money+effort so far. [they're not paying me to say this :)]

[Drdrdrq]

A bit off-topic, but does anyone know how good this[0] course is?

[0] https://www.udacity.com/course/deep-learning--ud730

[papaver]

i was quite put off by it. i feel like the teaching technique is pretty poor and the focus in on all the wrong things. mainly the tech gets in the way for learning. i don't want to figure out how to learn numpy when i'm trying to learn how to understand deep learning, that in itself is hard enough. i quite after a week (i did the stanford course first and this was going to be my second).

i would recommend the coursera course by andrew ng. i had an amazing time. the code stays out of your way and he walks you through the algorithms and explains the theory very well.

i just started the fast.ai by jeremy howard, and literally have been blown away but the course. it is AMAZING! by lesson 3 i'm able to build cnn models and score on top 20% in kaggle competitions. not bad for a complete novice. HIGHLY RECOMMENDED.

once im done with the fast.ai course i may look back around to google's deep learning course. i think it may be easier for more experienced users to digest its info.

https://www.coursera.org/learn/machine-learning

http://course.fast.ai/

Edit: added fast.ai link

[findjashua]

Felt very rushed for a beginner, okay if you have some background.

Personally, I found Stanford dl courses (image classification, nlp) to be much more suitable for beginners.

[krashidov]

I'm also a web developer and I plan on taking the SDC nanodegree program in the February.

Ideally, I want to work on self driving cars or AI in my day to day job, but I don't want to get my hopes up. Do you think that after you complete the nanodegree you will attempt to change your career to an SDC engineer or AI/ML engineer? Or is this just meant to fulfill a curiosity of yours?

[glidek]

As someone who's interested in taking the Udacity course, could you understand the course better by answering the following questions? Do you think the course prepares you enough find a Self-Driving developer job? Would you learn enough to compete/work along side people who got their Masters/PhD in Machine Learning? Appreciate your input.