Q.Autoencoder methods

A. Autoencoder is a type of neural network where the output layer has the same dimensionality as the input layer. In simpler words, the number of output units in the output layer is equal to the number of input units in the input layer. Various techniques exist to prevent autoencoders from learning the identity function and to improve their ability to capture important ' information and learn richer representations. 1.Sparse autoencoder (SAE) 2. Denoising autoencoder (DAE) 3. Contractive autoencoder (CAE) 4. Principal component analysis.


Q. L1 and L2 regularization?


A. L1 regularization gives output in binary weights from 0 to 1 for the model's features and is adopted for decreasing the number of features in a huge dimensional dataset. L2 regularization disperse the error terms in all the weights that leads to more accurate customized final models.


Q. How to measure the Euclidean distance betweeen the two arrays in numpy?

A. Euclidean distance is defined in mathematics as the magnitude or length of the line segment between two points. There are multiple methods for measuring the euclidean methods.

Method 1. In this method, we first initialize two numpy arrays. Then, we use linalg.norm() of numpy basically to compute the euclidean distance directly.

Method 2. In this method, we first initialize two numpy arrays. Then, we take the difference of the two arrays, compute the dot product of the result, and transpose of the result. Then we take the square root of the answer. This is another way to implement Euclidean distance.

Method 3. In this method, we first initialize two numpy arrays. Then, we compute the difference of these arrays and take their square. We take the sum of the squared elements, and after that, we take the square root in the end. This is another way to implement Euclidean distance.


Q.What are the support vectors in SVM?

A. Support vectors are data points that are closer to the hyperplane and influence the position and orientation of the hyperplane. Using these support vectors, we maximize the margin of the classifier. Deleting the support vectors will change the position of the hyperplane. These are the points that help us build our SVM.


Q. How do you handle categorical data?

A. One-Hot Encoding is the most common, correct way to deal with non-ordinal categorical data. It consists of creating an additional feature for each group of the categorical feature and mark each observation belonging (Value=1) or not (Value=0) to that group.


Q. What is coerrelation?

A.Correlation is a statistical measure that expresses the extent to which two variables are linearly related (meaning they change together at a constant rate). It's a common tool for describing simple relationships without making a statement about cause and effects


Q. What is covariance?

A. Covariance is nothing but a measure of correlation. Covariance is a measure of how much two random variables vary together. It’s similar to variance, but where variance tells you how a single variable varies, co variance tells you how two variables vary together