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!

You May Also Like

1/ Some initial thoughts on personal moats:

Like company moats, your personal moat should be a competitive advantage that is not only durable—it should also compound over time.

Characteristics of a personal moat below:


2/ Like a company moat, you want to build career capital while you sleep.

As Andrew Chen noted:


3/ You don’t want to build a competitive advantage that is fleeting or that will get commoditized

Things that might get commoditized over time (some longer than


4/ Before the arrival of recorded music, what used to be scarce was the actual music itself — required an in-person artist.

After recorded music, the music itself became abundant and what became scarce was curation, distribution, and self space.

5/ Similarly, in careers, what used to be (more) scarce were things like ideas, money, and exclusive relationships.

In the internet economy, what has become scarce are things like specific knowledge, rare & valuable skills, and great reputations.