MNIST for ML Beginners: The Bayesian Way
alpha-i.co
MNIST for ML Beginners: The Bayesian Way
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Re: MNIST for ML Beginners: The Bayesian Way
#2Worth pointing out that the mean field approximation used in this example means the posteriors are uncorrelated with each other - exactly what the author finds in the "joint posterior distribution" figure.
Re: MNIST for ML Beginners: The Bayesian Way
#3I wish someone would come up with a tutorial for ML for the mathematically challenged. Something more practical and less theoretical.
Re: MNIST for ML Beginners: The Bayesian Way
#4I wish someone would come up with a tutorial for ML for the mathematically challenged. Something more practical and less theoretical.
I understand and am sympathetic towards the sentiment, but honestly, its a bit like asking for a tutorial on swimming that does not involve water.
Something like that can be written, but it wont be very useful. Most of the simple stuff, the non-mathematical parts would get automated away. You don't want to be in a position where you are competing with someone's commodity script (sometimes just a for loop), unless the situation calls for desperate measures.
A bodybuilder got to lift them weights.
One genuine scenario could be that you personally do not do ML but want to evaluate/understand what your hires are doing. Even then, its hard to do avoid the math if you want to do a semi-decent job.
Re: MNIST for ML Beginners: The Bayesian Way
#5I wish someone would come up with a tutorial for ML for the mathematically challenged. Something more practical and less theoretical.
I understand and am sympathetic towards the sentiment, but honestly, its a bit like asking for a tutorial on swimming that does not involve water. Something like that can be written, but it wont be very useful. Most of the simple stuff, the non-mathematical parts would get automated away. You don't want to be in a position where you are competing with someone's commodity script (sometimes just a for loop), unless the…
I get it, and I've actually used the same metaphor just a couple days ago. What I mean though, is that the maths are OK, and although the heavy notation goes over my head (as I'm not academic), I understand the ideas. But I'm lacking some practical examples of how this is put to use. I understand how neural networks work for example, but I do not understand the maths behind how they work. There was (is?) a site called ai-junkie which used (does?) have very practical and layman docs about A.I. before ML became the buzz word du jour.
Re: MNIST for ML Beginners: The Bayesian Way
#6Earlier quoted context omitted.
I understand and am sympathetic towards the sentiment, but honestly, its a bit like asking for a tutorial on swimming that does not involve water. Something like that can be written, but it wont be very useful. Most of the simple stuff, the non-mathematical parts would get automated away. You don't want to be in a position where you are competing with someone's commodity script (sometimes just a for loop), unless the…
I get it, and I've actually used the same metaphor just a couple days ago. What I mean though, is that the maths are OK, and although the heavy notation goes over my head (as I'm not academic), I understand the ideas. But I'm lacking some practical examples of how this is put to use. I understand how neural networks work for example, but I do not understand the maths behind how they work. There was (is?) a site calle…
Oh I do understand you. The manual for driving a car has to be different from the manual for designing a car. I believe you are looking for a manual to drive the car where you don't really need a lot of visibility into the inner workings.
A problem is that ML is not quite as mature as a car yet, so the driving manuals will be a bit on the thinner / shallower side.
> I understand how neural networks work
Quickly write that down please, that would do the world a favor. Researchers are still grappling with the question 'why the hell does this freaking thing work as well as it does, when it does'.
Re: MNIST for ML Beginners: The Bayesian Way
#7Earlier quoted context omitted.
I get it, and I've actually used the same metaphor just a couple days ago. What I mean though, is that the maths are OK, and although the heavy notation goes over my head (as I'm not academic), I understand the ideas. But I'm lacking some practical examples of how this is put to use. I understand how neural networks work for example, but I do not understand the maths behind how they work. There was (is?) a site calle…
Oh I do understand you. The manual for driving a car has to be different from the manual for designing a car. I believe you are looking for a manual to drive the car where you don't really need a lot of visibility into the inner workings. A problem is that ML is not quite as mature as a car yet, so the driving manuals will be a bit on the thinner / shallower side. > I understand how neural networks work Quickly write…
Haha, I meant, the practical idea behind their usage. The example of OCR via a NN was very good in drilling that concept into my head (many inputs leading down to output).
The HOW (in capitals) they work bit - I'm not going there. This thread here got me searching and I'm reading through the basic TensorFlow docs. That's, so far, sinking in.
Re: MNIST for ML Beginners: The Bayesian Way
#8Earlier quoted context omitted.
I understand and am sympathetic towards the sentiment, but honestly, its a bit like asking for a tutorial on swimming that does not involve water. Something like that can be written, but it wont be very useful. Most of the simple stuff, the non-mathematical parts would get automated away. You don't want to be in a position where you are competing with someone's commodity script (sometimes just a for loop), unless the…
I get it, and I've actually used the same metaphor just a couple days ago. What I mean though, is that the maths are OK, and although the heavy notation goes over my head (as I'm not academic), I understand the ideas. But I'm lacking some practical examples of how this is put to use. I understand how neural networks work for example, but I do not understand the maths behind how they work. There was (is?) a site calle…
Honestly, the best thing you can do is try to implement your own shitty neural net with only Python + Numpy, from scratch, with only a basic understanding of the math. It will make most of the math very concrete very fast.
Re: MNIST for ML Beginners: The Bayesian Way
#9Earlier quoted context omitted.
I get it, and I've actually used the same metaphor just a couple days ago. What I mean though, is that the maths are OK, and although the heavy notation goes over my head (as I'm not academic), I understand the ideas. But I'm lacking some practical examples of how this is put to use. I understand how neural networks work for example, but I do not understand the maths behind how they work. There was (is?) a site calle…
Honestly, the best thing you can do is try to implement your own shitty neural net with only Python + Numpy, from scratch, with only a basic understanding of the math. It will make most of the math very concrete very fast.
Cannot upvote this enough.
I think this is the only way to really grok backpropagation. The hours of staring at the update formula till your eyes glaze over the subscripts and superscripts and the summations would not give you as good an understanding as implementing a toy neural net with just a single hidden layer. Its actually a whole lot easier than parsing those low-level notation. It can be done better with high level notation but then you would need familiarity with the relevant mathematical abstractions.
Re: MNIST for ML Beginners: The Bayesian Way
#10I wish someone would come up with a tutorial for ML for the mathematically challenged. Something more practical and less theoretical.
That's been done:
Can't recommend this enough.