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!