Quick question from data science and machine learning interview Part 2

1.What is the meaning of term weight initialization in neural networks?

In neural networking, weight initialization is one of the essential factors. A bad weight initialization prevents a network from learning. On the other side, a good weight initialization helps in giving a quicker convergence and a better overall error. Biases can be initialized to zero. The standard rule for setting the weights is to be close to zero without being too small.

2.What is Cross-validation in Machine Learning?

Cross-validation allows a system to increase the performance of the given Machine Learning algorithm. This sampling process is done to break the dataset into smaller parts that have the same number of rows, out of which a random part is selected as a test set and the rest of the parts are kept as train sets. Cross-validation consists of the following techniques:

• Holdout method

• K-fold cross-validation

• Stratified k-fold cross-validation

• Leave p-out cross-validation

3.What is a Self-Join?

A self-join is a type of join that can be used to connect two tables. As a result, it is a unary relationship. Each row of the table is attached to itself and all other rows of the same table in a self-join. As a result, a self-join is mostly used to combine and compare rows from the same database table.

4. What are the types of views in SQL?

In SQL, the views are classified into four types. They are:

Simple View: A view that is based on a single table and does not have a GROUP BY clause or other features.

Complex View: A view that is built from several tables and includes a GROUP BY clause as well as functions.

Inline View: A view that is built on a subquery in the FROM clause, which provides a temporary table and simplifies a complicated query.

Materialized View: A view that saves both the definition and the details. It builds data replicas by physically preserving them.