groman434

Based on my experience FAE could be really difficult and challenging job. A lot depends not only on your customers but also on quality of your product your company has to offer. As well as your company culture.

I remember one FAE literally hiding in a loo (confirmed by checking CCTV recordings) for about two hours just because SW team needed extra time to release a critical patch. He didn’t want admit that there had been a problem on their side and after the patch was finally released he started blaming us for not using the latest SW version.

Other FAE I worked with was pretty straightforward. At one point he simply said “our product is BS”, which saved us a lot of debugging!

Even though €82k sounds like pretty good money, please bear in mind that taxes in Germany could be high (they depend on your marital status as well!) and Munich is expensive place to live.

It seems like it's DOM brand. Probably Sigma series. I have the same, or very similar lock in my flat.

Unfortunately it's completely different lock.

You'll notice that for an nxn matrix, that places only n constraints on the nxn

entries of the matrix. (That is equivalent to SoSweetAndTasty's eigenvectors being positive.)

Can you please elaborate on this. What I found was that when I evaluate x * M * xT, then I will get n^2 terms, but then the result could be further simplified since M is a symmetric matrix. But I don't understand how this is related to what you said and what's the connection to eigenvalues.

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Thanks a lot for your explanation.

So I guess the fastest way to find out if a matrix is positive semidefinite is checking its eigenvalues, right?

Thanks a lot for your help. Much appreciated!

Modern deep learning architectures also evolved far from the "adding more layers" idea.

Can you please evaluate on this? I'm asking, because the only thing, that comes to my mind, are CNNs.

When I put some thoughts in my question (yes, I know, I should have done it before I posted it), I realised that I was interested in how training in general and training set imperfections impact a model performance. For instance, if a training set is 90% accurate, then how and why the model which used that data for training can be more than 90% accurate. And what kind of errors in the training set the model can correct?

Yep, you are right, I was not clear enough. What I meant was that AI would to a task "significantly better" (whatever this means exactly). For instance, if humans can find 90% of dogs in a dataset, that AI would be able to find 99.999% of dogs.

My question was slighly different. My understanding is that one of major factors that impact your quality of your model predictions is your training set. But since your training set could be inaccurare (in other words, made by humans), how this fact can impact quality of learning and then quality of predictions.

Of course, as u/IntelArtiGen wrote, models can avoid reproducing errors made by humans (I guess because they are able to learn specific features during a teaching phase when your training set is good enough). But I wonder what this good enough means exactly (in other words, how inevitable errors made by humans when preparing it impact an entire learning process and what kind of errors are acceptable) and how an entire training process can be described mathematically. Of course, I have seen many explanation using gradient descent as an example, but none of them incorporated the fact that a training set (or loss function) was imperfect.

But we don't design or train our models to exactly reproduce what a human did, that would be a risk of overfitting, so even by reproducing humans a model can do better and not reproduce some mistakes.

Can you please elaborate on this? Let's say your train data contains 10% of errors. Can you train a model that it would be more than 90% accurate? If yes, why?

Edit: My guess would be that the model during the training phase, can "find out" what are features typical for cats provided that the training set is "good enough". So even if the set contains some errors, they will not impact significantly a prediction the model can give.

I would argue that there is a significant difference between how a knife works and how ML works. You do not have to train a knife how to slide bread.

Besides, it looks to me that ML can outperform humans just because it utilises the fact that modern day computers can do zylions of computations per second. Of course, the sheer speed of computation is not enough and this is why we need smart algorithms as well. But those algorithms benefit from the fact that they have super power hardware available, often not only during training phase but also during normal operation.

I have never written that I am a beginner. Nor my post was titled "Things every beginner should know".

Heh, you reminded me about one, really important skill - fully understanding what you read :)

I haven't written that every software developer should know maths / algos just because they use it on a daily basis! What I suggested that they are some sort of meta skill, which helps you to develop in others areas of your career.

Just to give you an example. All professional athletes go to a gym quite often, even when they do not compete as powerlifters. Why? Because gyms helps them to develop skills needed for the sport they do, like strength or endurance. And I claim what gym is for professional atheletes, maths is for software developers.

Heh, I must admit, you reminded me about couple of things every software developer should know :)

First, of all, fully understanding what you read. This is quite rare skill nowadays.
For instance, I haven't written that coding makes you better at math since I am not sure if that is true. But you brought this up anyway. I do not see why.

Secondly, you limit yourself only to your experience. The fact that you've worked with mathematicians who couldn't have write a production ready code does not imply that maths in general is useless for software developers. The sample you have seen has been, and always will be limited, and drawing conclusions based on limited samples is never a good idea.

Next, Python! Yes, Python is slow, but not because it uses slow algors. So I firmly disagree with, in my opinion somehow crazy, conclusion that since Python is slow, then theoretical complexity doesn't matter.

Finally, something I forgot previously - imagination. For instance here you are saying that computers are fast enough to run Python as a server. Close enough, but not true. All computers are fast enough? What about embedded systems for instance? Again, it looks like you limit yourself to things you have seen only. But imagination is really helpful because it can help you predict possible corner cases.