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In object oriented programming, we may want our object to get initialized with some specific properties, or we may need to do some operations every time an object is instantiated. To do such things, we use constructors. In simple words, a constructor is defined inside the class that contains the code to instantiate the object of a class in java.

Write a program to find the minimum number of operations required to convert string X to string Y. You have the following three operations permitted on a string: 1) Insert a character 2) Delete a character 3) Replace a character. The edit distance between two strings is the minimum number of operations (insertions, deletions, or substitutions of characters) required to transform one string into the other.

The flood fill problem is a way to fill a region of connected pixels with a new colour, starting from a given pixel in an image. You are given an image represented by an m x n grid of integers, where the image[i] represents the pixel value at position (i, j). You are also given the coordinates of a starting pixel (x, y) and a new colour to use for the flood fill operation.

The key idea behind using a stack is that it allows for easy access to the most recently added element and easy removal of that element, which can be useful in situations where you need to keep track of a history of actions or reverse actions. So based on various use cases, there are several applications of the stack in coding problem-solving and computer programming.

Artificial Neural Networks (ANNs), popularly known as Neural Networks, are supervised Machine Learning algorithms capable of solving both classification and regression problems. Here we have discussed the analogy from human brain, terms used to define any neural network, what are the things present in a schematic diagram of ANN, advantages and disadvantages of ANN with respect to other machine learning algorithms and lastly what are the practical use-cases of ANN.

In order to fully grasp the concept of a Neural Network, it is crucial to understand the various components that make up a Neural Network. In this article, we delve into the key components of an Artificial Neural Network, including Neurons, Input Layers, Output Layers, Hidden Layers, Connections, Parameters, Activation Functions, Optimization Algorithms, and Cost Functions. These components work together to solve both classification and regression problems in Machine Learning.