<story><title>Deep Learning Interviews book: Hundreds of fully solved job interview questions</title><url>https://github.com/BoltzmannEntropy/interviews.ai</url></story><parent_chain><item><author>uoaei</author><text>In part because ML fails silently by design. Even if the code runs flawlessly with no errors, the outputs could be completely bunk, useless, or even harmful, and you won&amp;#x27;t have any idea if that is true just from watching The Number go down during training. It&amp;#x27;s not enough to know how to build it but also &lt;i&gt;how it works&lt;/i&gt;. It&amp;#x27;s the difference between designing the JWST and assembling it.</text></item><item><author>mcemilg</author><text>The ML&amp;#x2F;DS positions highly competitive these days. I don&amp;#x27;t get why ML positions requires hard preparations for the interviews more than other CS positions while you do similar things. People expect you to know a lot of theory from statistics, probability, algorithms to linear algebra. I am ok with knowing basic of these topics which are the foundations of ML and DL. But I don&amp;#x27;t get to ask eigenvectors and challenging algorithm problems in an ML Engineering position at the same while you already proof yourself with a Masters Degree and enough professional experience. I am not defending my PhD there. We will just build some DL models, maybe we will read some DL papers and maybe try to implement some of those. The theory is the only 10% of the job, rest is engineering, data cleaning etc. Honestly I am looking for the soft way to get back to Software Engineering.</text></item></parent_chain><comment><author>dekhn</author><text>ML doesn&amp;#x27;t just fail silently by design.  because ML is based on error minimization, it fails in a way that is maximally hard to tell from random garbage.  This is, remarkably, a subtlety that is lost on most people, which is a real surprise- my introduction to this was in structural biology, where you always do hold-outs and check the performance on the hold-out set before overfitting is such a problem.</text></comment>
<story><title>Deep Learning Interviews book: Hundreds of fully solved job interview questions</title><url>https://github.com/BoltzmannEntropy/interviews.ai</url></story><parent_chain><item><author>uoaei</author><text>In part because ML fails silently by design. Even if the code runs flawlessly with no errors, the outputs could be completely bunk, useless, or even harmful, and you won&amp;#x27;t have any idea if that is true just from watching The Number go down during training. It&amp;#x27;s not enough to know how to build it but also &lt;i&gt;how it works&lt;/i&gt;. It&amp;#x27;s the difference between designing the JWST and assembling it.</text></item><item><author>mcemilg</author><text>The ML&amp;#x2F;DS positions highly competitive these days. I don&amp;#x27;t get why ML positions requires hard preparations for the interviews more than other CS positions while you do similar things. People expect you to know a lot of theory from statistics, probability, algorithms to linear algebra. I am ok with knowing basic of these topics which are the foundations of ML and DL. But I don&amp;#x27;t get to ask eigenvectors and challenging algorithm problems in an ML Engineering position at the same while you already proof yourself with a Masters Degree and enough professional experience. I am not defending my PhD there. We will just build some DL models, maybe we will read some DL papers and maybe try to implement some of those. The theory is the only 10% of the job, rest is engineering, data cleaning etc. Honestly I am looking for the soft way to get back to Software Engineering.</text></item></parent_chain><comment><author>borroka</author><text>But the OP was asking something different, that is why someone should excessively focus on theory, when, by the way, DL theory is very far from being solid and trial and error in ML and AI is the common way of operating.&lt;p&gt;The &amp;quot;model is in place, but I have no clue what&amp;#x27;s doing and so it can fail without me understanding when and how is straw-man&amp;quot;. Especially for supervised learning, that is, we have a label for data, it is immediately clear whether the output of the model is &amp;quot;bunk, useless, or even harmful&amp;quot;. There is no &amp;quot;fail silently by design&amp;quot;.&lt;p&gt;I have been working in the field for almost 20 years in academia and in industry and it is not that I starting every PCA thinking about eigenvectors and eigenvalues and if you ask me now without preparing what are those, I would be between approximately right and wrong. But I fit many, many very accurate models.</text></comment>