<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>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>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></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>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>hintymad</author><text>A reason for such requirements is similar to that that software engineers need to leetcode hard: supply and demand. Prestigious companies get hundreds, if not thousands, of applications every day. The companies can afford looking for candidates who have raw talent, such as the capability of mastering many concepts and being able solve hard mathematical problems in a short time. Case in point, you may not need to use eigenvectors directly in the job, but the concept is so essential in linear algebra and I as a hiring manager would expect a candidate to explain and apply it in their sleep. That is, knowing eigenvector is an indirect filter to get people who are deeply geeky. Is it the best strategy for a company? That&amp;#x27;s up to discussion. I&amp;#x27;m just explaining the motives behind such requirements.</text></comment>