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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One of the most successful stock trader with special focus on cash stocks and who has a very creative mind to look out for opportunities in dark times

Covering one of the most unique set ups: Extended moves & Reversal plays

Time for a 🧵 to learn the above from @iManasArora

What qualifies for an extended move?

30-40% move in just 5-6 days is one example of extended move

How Manas used this info to book


Post that the plight of the


Example 2: Booking profits when the stock is extended from 10WMA

10WMA =


Another hack to identify extended move in a stock:

Too many green days!

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