<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>nerdponx</author><text>Maybe &amp;quot;eigenvectors&amp;quot; is a bad example, because it&amp;#x27;s a pretty foundational linear algebra concept.&lt;p&gt;But there is a threshold where it stops being a test of foundational knowledge and starts being a test of arbitrary trivia, and favors who has the most free time to study and memorize said trivia.</text></comment>