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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@EricTopol @NBA @StephenKissler @yhgrad B.1.1.7 reveals clearly that SARS-CoV-2 is reverting to its original pre-outbreak condition, i.e. adapted to transgenic hACE2 mice (either Baric's BALB/c ones or others used at WIV labs during chimeric bat coronavirus experiments aimed at developing a pan betacoronavirus vaccine)

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@NBA @StephenKissler @yhgrad 2. High Probability of serial passaging in Transgenic Mice expressing hACE2 in genesis of SARS-COV-2


@NBA @StephenKissler @yhgrad B.1.1.7 has an unusually large number of genetic changes, ... found to date in mouse-adapted SARS-CoV2 and is also seen in ferret infections.
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@NBA @StephenKissler @yhgrad We adapted a clinical isolate of SARS-CoV-2 by serial passaging in the ... Thus, this mouse-adapted strain and associated challenge model should be ... (B) SARS-CoV-2 genomic RNA loads in mouse lung homogenates at P0 to P6.
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