The toughest data science interview I ever had

I got bombarded for 45 minutes with theoretical questions:

🔸 Entropy
🔸 KL divergence, other divergences
🔸 Kolmogorov complexity
🔸 Jacobian and Hessian
🔸 Linear independence
🔸 Determinant

Continued 👇

🔸 Eigenvalues and Eigenvectors
🔸 SVD
🔸 The norm of a vector
🔸 Independent random variables
🔸 Expectation and variance
🔸 Central limit theorem

👇
🔸 Gradient descent and SGD
🔸 Other optimization methods
🔸 The dimension of gradient and hessian for a neural net with 1k params
🔸 What is SVM
🔸 Linear vs non-linear SVM
🔸 Quadratic optimization

👇
🔸 What to do when a neural net overfits
🔸 What is autoencoder
🔸 How to train an RNN
🔸 How decision trees work
🔸 Random forest and GBM
🔸 How to use random forest on data with 30k features
🔸 Favorite ML algorithm - tell about it in details

That was tough!

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I’m torn on how to approach the idea of luck. I’m the first to admit that I am one of the luckiest people on the planet. To be born into a prosperous American family in 1960 with smart parents is to start life on third base. The odds against my very existence are astronomical.


I’ve always felt that the luckiest people I know had a talent for recognizing circumstances, not of their own making, that were conducive to a favorable outcome and their ability to quickly take advantage of them.

In other words, dumb luck was just that, it required no awareness on the person’s part, whereas “smart” luck involved awareness followed by action before the circumstances changed.

So, was I “lucky” to be born when I was—nothing I had any control over—and that I came of age just as huge databases and computers were advancing to the point where I could use those tools to write “What Works on Wall Street?” Absolutely.

Was I lucky to start my stock market investments near the peak of interest rates which allowed me to spend the majority of my adult life in a falling rate environment? Yup.