
GAN – Generative Adversary Networks (2018) - rfreytag
https://medium.com/@jonathan_hui/gan-whats-generative-adversarial-networks-and-its-application-f39ed278ef09
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TrackerFF
While I appreciate people trying to publish their own explanations of how
various NN/Deep Learning architectures work, I think a fully coded article
would be much better for the people these are aimed at.

It's like with research papers: The intuitive part isn't _that_ hard to grok
(sometimes they are remarkably intuitive) - it's the translation from
equations to working code.

I have one friend that's trying to learn ML, and he has a solid mathematical
background, but mostly on a theoretical level (i.e not applied). He could
explain perfectly well how various neural networks worked, but he really,
really struggled with coding them.

In fact, I wish every researcher would put a link to their code repository

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jsinai
It depends on where you are in terms of your learning and background. Some
people will find value in intuitive blogposts which complement research
papers. Others can “grok” this as you say and will find more value in code.
Yet others can “grok” the code if only they had the intuitive understanding.
You can see where I am getting: everyone has different backgrounds and needs
and different articles will appeal to different audiences. There is no one
audience to appeal to.

On the note of code: this tends to be more subjective than the maths in a
paper. There are lots of design decisions that can affect the overall
structure of the code. Then there is the trade off between readability and
performance. Readable code for blogs is probably not the code you would use in
practice.

Personally I find it easier to establish the ground truth in the mathematics
than to try and figure out the underlying truth from the code. And if you
can’t do it yourself, then that’s where the intuitive blogposts help. After
that you can choose your own libraries, frameworks, programming design
decisions and teachers (including other blogposts) to help you get the code.

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amelius
I was thinking, can we invent a more formal, common graphical representation
of how neural networks work, just like how we have a graphical representation
of electronic networks?

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vaylian
One of the major selling points of neural networks is that you can model/learn
relationships that are very hard to explicitly write down. Also keep in mind
that neural networks often work with floating point values to scale input
values. Sure, you can write down these floating point values in a diagram, but
the numbers themselves will not tell you why they have the magnitude that they
have.

With that being said, there is research happening that tries to find better
tools to understand the reasoning of neural networks. And sometimes we might
actually see and understand a general decision pattern of a NN that we could
adopt for a simpler model. But in many cases it will not be possible to come
up with a simpler model because some phenomena have an intrinsic complexity.

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iyn
On a related note, there's a "Interpretable Machine Learning" approach that
tries to provide more insight into the "whys" of ML. (I'm not en expert and
I've learnt about this quite recently)

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jimijazz
Nice article. I specially enjoyed your collection of applications linked at
the bottom.

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nightcracker
Dear Medium developers, let me summarize my experience with Medium in one
image:

[https://i.imgur.com/E0MYinp.png](https://i.imgur.com/E0MYinp.png)

To blog writers, please reconsider hosting your blog on Medium.

~~~
jmuguerza
You may have posted personal info there.

~~~
nightcracker
I'm aware it contains my name and email, that's okay.

~~~
nightcracker
And someone took the opportunity to sign me up for a bunch of unwanted
newspapers... My email hasn't been private for a long time, and so are many
other people's emails. That is not an invitation to be an asshole, nor am I a
'stupid fuck'.

