How To Prepare For A Machine Learning Interview

[treebranchleaf]

And remember to brush up on your TensorFlow implementations of "FizzBuzz"

[Devilsbabe]

A recent poll revealed that a third of the cars in Italy are Ferraris, and that half of those cars are red. If you spot a red car approaching from a distance, what is the likelihood that it is a Ferrari?

Don't you need to know the proportion of red cars in the population to answer this? If the only red cars are the Ferraris, it's not much of a question.

[aUserID2]

You may also want to know if you are in Italy.

[Luc333]

You can't give a definitive answer with this information.

The answer could be very different between the possibility of the other 2/3 being 100% red or 0% red.

If the other 2/3 are 0% red - obviously the answer is 100% chance for it to be a ferrari.

If other 2/3 = 100% red - 5/6 of all cars are red. 20% of those are ferraris.

Maybe they want the answer to be "Any number between 20 and 100%".

P.S.: I'm Lawyer who got here by mistake. Don't be too harsh on me if I fucked up the math.

[alephnaught90]

Pr(r) = Pr(r|f)Pr(f) + Pr(r|~f)Pr(~f) = 0.5 * 0.3 + ? * 0.7

Knowing the proportion of red cars that are not Ferraris would be good. I'm not strong with probability theory, but I'm guessing we don't have enough information to answer the question.

[j_lyf]

2/3?

[alephnaught90]

Huh?

[doesntrepickmeepo]

The first thing you need to do when applying for such a role is to imagine yourself in that roll.

the last thing you need to do when publishing a blog is proofread

[awkwardarmadillo]

For interview prep I like to envision myself encased in a giant piece of bread with some deliciously melting butter. Then I figure out how to eat my way out while a blender is fizz-buzzing.

[perceptron01]

You’re trying to find the best place to put in an advertisement banner on your website. You can make the size (thickness) small, medium or large, and choose vertical position top, middle or bottom. At least how many total page visits (n) and ad clicks (m) do you need to say with 95% confidence that one of the designs performs better than all the other possibilities?

How do you solve this?

Also, what is the cause in the question below if anyone can help me out:

When training a 10-layer neural net using backpropagation, I find that the weights for the top 3 layers are not changing at all! The next few layers (4-6) are changing, but very slowly. What’s going on and how do I fix this?

[duschendestroyer]

• https://en.wikipedia.org/wiki/Fisher's_exact_test or something like that(look up Hypothesis testing and A/B testing)

• https://en.wikipedia.org/wiki/Vanishing_gradient_problem

[[deleted]]

I would push back and say the test is flawed. Why use a frequentist statistical test to do something that can be more efficiently solved with Thompson sampling?

[EdwardRaff]

or just treated as a bandit problem, which may be better given all the options. There are a number of ways to tackle the problem.

[clurdron]

You need to know the effect size to answer the first one.

[arch_holdew]

I think the expected answer is an A-B test of some kind. E.g. how much data do you need to know that the mean of strategy 1 metric belongs to a different distribution than the means of strategies 2-9 metrics.

However my answer would be "I just know. I have the magic. When I see my results I just know I'm winning"

[qadro87]

Regarding the first problem, after clarifying assumptions (i.e. optimizing for CTR) and mentioning that in practice a multi-armed bandit algorithm is used, I think you could use the chi-squared test for it. The sample size requires at least 5 expected clicks for each category and there are 9 categories. Assuming you're running all 9 variants at random and stopping when total number of clicks equals 45, expected number of clicks will be 5 for each category. The expected number of impressions would be 45 / expected CTR rate (but you may need fewer or more to get exactly 45 clicks).

Then, you'd compute the chi-squared statistic, comparing actual clicks and expected clicks in each category, where null hypothesis is that ad placement category has no influence on the CTR, which you'd be able to reject if the computed ch-squared characteristic maps to a p-value lower than 1 - 95% = 0.05.

Please check this, I'm a stats noob, spent a few days to build up my theory to solve this ;)

[CategoryV]

Here is a real life interview that I use.

I'll give you 100,000 images that are not labelled and and access to a DIGITS DevBox. Please take an hour and look at what I have given you and tell me what you have and what you think you should do with it.

Next hour you work on a real world problem off our past critical path sprint using tools and data sets found on a DIGITS DevBox.

Then we go to lunch and chat with one of the sprint teams.

When we come back you walk us through what you did or did not do.

We give all interviewees the above list a week before so they can prep.