Toss
모니터링 지표
B) Machine Learning
- Linear independence
- determinant
- eigenvalue and Eigenvectors
- SVD
- The norm of a vector
- Independent random variables
- Expectation and variance
- central limit theorem
- entropy, what it means intuitively, formula
- KL-Divergence, other divergences
- Kolmogorov complexity
- Jacobian and Hessian matrix
- Gradient descent and SGD
- Other optimization methods
- NN with 1k params - what’s dimensionality of a gradient and hessian
- What is SVM, linear vs non-linear SVM
- Quadratic optimization
- NN overfits - what to do
- 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