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Andrew Ng updates his Machine Learning course

deeplearning.ai

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Re: Andrew Ng updates his Machine Learning course

#41

Post by londons_explore »

BeetleB wrote: post #14
lern_too_spel wrote: post #10

Earlier quoted context omitted.

The programming assignments were one or two lines in Octave. They'll turn into 10 lines of Python with indentation errors. Python is a worse pedagogical language for any course in applied linear algebra.

The programming assignments in the original course were mostly useless. They provided you with a template with 90% of the problem solved, and you just had to enter an equation to solve the problem.

But you also had to understand the rest of the code in the template.

And reading code tends to be a quicker way to learn roughly how something works than writing it from scratch.

Re: Andrew Ng updates his Machine Learning course

#42

Post by MafellUser »

xwdv wrote: post #15

Although this is the best course on ML, is it really practical for anything? Has anyone built products for things they’ve learned from this course?

I took this course as a defensive mechanism against BS at work, especially when the consulting Data Scientists were around. In that sense it's super practical.

ML is dominated by gigantic datasets and massive computing powers, something individuals will not have a lot of.

Re: Andrew Ng updates his Machine Learning course

#43

Post by [deleted] »

UmbertoNoEco wrote: post #25
xwdv wrote: post #15

Although this is the best course on ML, is it really practical for anything? Has anyone built products for things they’ve learned from this course?

No. The ugly truth is that these courses will be useless to 99% of the people. Machine learning is dominated by big corporations with gigantic amounts of data and processing power. If you want to work in one of them or create competing ML companies you need pedigree (a PhD from a well know university), and those guys arent taking courses with fake credentials. You could use ML in your job/company but then you dont ne…

[deleted]

Re: Andrew Ng updates his Machine Learning course

#44

Post by lern_too_spel »

DiogenesKynikos wrote: post #28
lern_too_spel wrote: post #10

Earlier quoted context omitted.

The programming assignments were one or two lines in Octave. They'll turn into 10 lines of Python with indentation errors. Python is a worse pedagogical language for any course in applied linear algebra.

Python is an easy language for beginners, and with numpy, it has excellent support for linear algebra.

Octave is an easy language for beginners and has excellent (less ceremony than numpy) support for linear algebra out of the box without having to learn any libraries. The point of the class isn't to teach you how to use libraries but to teach you at a high level how to use gradient descent to optimize parameterized models. Once you understand how it works, it is easy to translate what you know to run well on different systems or to use existing frameworks already implemented on different systems.

Re: Andrew Ng updates his Machine Learning course

#46

Post by Choco31415 »

xwdv wrote: post #15

Although this is the best course on ML, is it really practical for anything? Has anyone built products for things they’ve learned from this course?

I used it to help learn ML before I could start taking the classes at my university and it was enough to land me a research position at the Air Force.

Admittedly I also bought textbooks and worked through tutorials as well.

Re: Andrew Ng updates his Machine Learning course

#47

Post by NelsonMinar »

xwdv wrote: post #15

Although this is the best course on ML, is it really practical for anything? Has anyone built products for things they’ve learned from this course?

I thought it was useful but awfully low level. For example I hope to never, ever implement backpropagation again; I'm going to use whatever code is in TensorFlow or PyTorch or whatever. But as a student I'm glad I did implement it myself, once, so I understand what is going on. More broadly it demystifies the black box of machine learning methods and you can see it for the giant pile of statistical categorizing functions that it is.

The most practical takeaway I got from Ng's course was the dangers of under and overfitting your data and techniques for detecting when you make that mistake.

Re: Andrew Ng updates his Machine Learning course

#48

Post by jcadam »

bowsamic wrote: post #39
nytesky wrote: post #33

Earlier quoted context omitted.

So sad to hear Mariah disparaged. I’m an Gen X engineer and Matlab is one of our first languages. Use it today still in aerospace but I would imagine Python suits software shops much better. Does Python handle matrix math as well?

MATLAB is still in heavy use in physics, mainly for experiments bc of simulink and the control systems toolbox

When I worked on the GPS III program back in the day, my job largely consisted of translating scientists' Matlab script into Ada.

Re: Andrew Ng updates his Machine Learning course

#50

Post by jcadam »

DoubleFree wrote: post #40
nytesky wrote: post #33

Earlier quoted context omitted.

So sad to hear Mariah disparaged. I’m an Gen X engineer and Matlab is one of our first languages. Use it today still in aerospace but I would imagine Python suits software shops much better. Does Python handle matrix math as well?

Matrix math in python is a bit clunkier, because matrices are not native to the language. That said, numpy, the standard for matrix math in python, is quite nice. Its documentation is, imo, miles ahead of matlab's and the APIs are a bit more sane.

This brings up a good point. If the goal is to understand the underlying concepts, it's quite possible Octave is a better tool. Matrix math is fairly clunky in any mainstream programming language.
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