Gunjan  Khaitan

Gunjan Khaitan


An Introduction to Backtracking Algorithm

This video gives an introduction to backtracking that could help you prepare for competitive programming. This backtracking algorithm tutorial aims to help learn data structures and algorithms.

This video will cover the following concepts:

00:00 Introduction
00:41 What is backtracking
01:16 N queens Problem
02:18 Implementation of N Queens Problem
21:24 Conclusion

What is Backtracking?
Backtracking is an algorithmic approach to solving a problem in a different way. To illustrate the issues, it uses a recursive method. To solve an optimization problem, we might state that backtracking is required.

What Is a Data Structure?
The short answer is: a data structure is a specific means of organizing data in a system to access and use. The long answer is a data structure is a blend of data organization, management, retrieval, and storage, brought together into one format that allows efficient access and modification. It’s collecting data values, the relationships they share, and the applicable functions or operations.

Why Is Data Structure Important?
The digital world processes an increasing amount of data every year. According to Forbes, there are 2.5 quintillion bytes of data generated daily. The world created over 90 per cent of the existing data in 2018 in the previous two years! The Internet of Things (IoT) is responsible for a significant part of this data explosion. Data structures are necessary to manage the massive amounts of generated data and are a critical factor in boosting algorithm efficiency. Finally, since nearly all software applications use data structures and algorithms, your education path needs to include learning data structure and algorithms if you want a career as a data scientist or programmer. Interviewers want qualified candidates who understand how to use data structures and algorithms, so the more you know about the concepts, the more comfortably and confidently you will answer data structure interview questions.

#datastructure #algorithms 

What is GEEK

Buddha Community

An Introduction to Backtracking Algorithm
Lina  Biyinzika

Lina Biyinzika


Introduction to Random Forest Algorithm: Functions, Applications & Benefits

Random Forest is a mainstream AI algorithm that has a place with the regulated learning strategy. It might be used for both Classification and Regression issues in ML. It depends on the idea of ensemble learning, which is a cycle of joining numerous classifiers to tackle an intricate issue and to improve the presentation of the model.

As the name proposes, “Random Forest is a classifier that contains different decision trees on various subsets of the given dataset and takes the typical to improve the perceptive precision of that dataset.”

Instead of relying upon one decision tree, the random forest takes the figure from each tree and subject it to the larger part votes of desires, and it predicts the last yield. The more noticeable number of trees in the forest prompts higher exactness and forestalls the issue of overfitting.

**Presumptions for Random Forest **

Since the random forest consolidates various trees to anticipate the class of the dataset, it is conceivable that some choice trees may foresee the right yield, while others may not. Yet, together, all the trees anticipate the right yield. In this way, beneath are two presumptions for a superior random forest classifier:

  • There should be some real qualities in the component variable of a dataset with a goal that the classifier can foresee precise outcomes as opposed to a speculated result.
  • The forecasts from each tree must have low connections.

#artificial intelligence #random forest #introduction to random forest algorithm #random forest algorithm #algorithm

Lina  Biyinzika

Lina Biyinzika


Introduction to Genetic Algorithm

What is Optimization?

  • Making something better.
  • Increase efficiency.

Optimization problem

  • A problem in which we have to find the values of inputs (also called solutions or decision variables) from all possible inputs in such a way that we get the “best” output values.
  • Definition of “best”- Finding the values of inputs that result in a maximum or minimum of a function called the objective function.
  • There can be multiple objective functions as well (depends on the problem).

Optimization algorithm

An algorithm used to solve an optimization problem is called an optimization algorithm.

Evolutionary Algorithms

Algorithms that simulate physical and/or biological behavior in nature to solve optimization problems.

Genetic Algorithm (GA)

  • It is a subset of evolutionary algorithms that simulates/models Genetics and Evolution (biological behavior) to optimize a highly complex function.
  • A highly complex function can be:
  • 1. Very difficult to model mathematically.
  • 2. Computationally expensive to solve. Eg. NP-hard problems.
  • 3. Involves a large number of parameters.

Background of GA

  • Introduced by Prof. John Holland in 1965.
  • The first article on GA was published in 1975.
  • GA is based on two fundamental biological processes:
  • 1. Genetics (by G.J. Mendel in 1865): It is the branch of biology that deals with the study of genes, gene variation, and heredity.
  • 2. Evolution (by C. Darwin in 1875): It is the process by which the population of organisms changes over generations.

Natural selection in Evolution

  1. A population of individuals exists in an environment with limited resources.
  2. Competition for those resources causes the selection of those fitter individuals that are better adapted to the environment.
  3. These individuals act as seeds for the generation of new individuals through recombination and mutation.
  4. Evolved new individuals act as initial population and Steps 1 to 3 are repeated.

Nature-GA Analogy

Structure of GA

GA vs Traditional Algorithms

Applications of GA

  • 1. Acoustics
  • 2. Aerospace Engineering
  • 3. Financial Markets
  • 4. Geophysics
  • 5. Materials Engineering
  • 6. Routing and Scheduling
  • 7. Systems Engineering

Problems with GA

  • 1. Population Selection Problem
  • 2. Defining Fitness Function
  • 3. Premature or rapid convergence of GA
  • 4. Convergence to Local Optima

#evolutionary-algorithms #data-science #genetic-algorithm #algorithm

A greedy algorithm is a simple

The Greedy Method is an approach for solving certain types of optimization problems. The greedy algorithm chooses the optimum result at each stage. While this works the majority of the times, there are numerous examples where the greedy approach is not the correct approach. For example, let’s say that you’re taking the greedy algorithm approach to earning money at a certain point in your life. You graduate high school and have two options:

#computer-science #algorithms #developer #programming #greedy-algorithms #algorithms

Tia  Gottlieb

Tia Gottlieb


KMP — Pattern Matching Algorithm

Finding a certain piece of text inside a document represents an important feature nowadays. This is widely used in many practical things that we regularly do in our everyday lives, such as searching for something on Google or even plagiarism. In small texts, the algorithm used for pattern matching doesn’t require a certain complexity to behave well. However, big processes like searching the word ‘cake’ in a 300 pages book can take a lot of time if a naive algorithm is used.

The naive algorithm

Before, talking about KMP, we should analyze the inefficient approach for finding a sequence of characters into a text. This algorithm slides over the text one by one to check for a match. The complexity provided by this solution is O (m * (n — m + 1)), where m is the length of the pattern and n the length of the text.

Find all the occurrences of string pat in string txt (naive algorithm).

#include <iostream>
	#include <string>
	#include <algorithm>
	using namespace std;

	string pat = "ABA"; // the pattern
	string txt = "CABBCABABAB"; // the text in which we are searching

	bool checkForPattern(int index, int patLength) {
	    int i;
	    // checks if characters from pat are different from those in txt
	    for(i = 0; i < patLength; i++) {
	        if(txt[index + i] != pat[i]) {
	            return false;
	    return true;

	void findPattern() {
	    int patternLength = pat.size();
	    int textLength = txt.size();

	    for(int i = 0; i <= textLength - patternLength; i++) {
	        // check for every index if there is a match
	        if(checkForPattern(i,patternLength)) {
	            cout << "Pattern at index " << i << "\n";


	int main() 
	    return 0;
view raw
main6.cpp hosted with ❤ by GitHub

KMP approach

This algorithm is based on a degenerating property that uses the fact that our pattern has some sub-patterns appearing more than once. This approach is significantly improving our complexity to linear time. The idea is when we find a mismatch, we already know some of the characters in the next searching window. This way we save time by skip matching the characters that we already know will surely match. To know when to skip, we need to pre-process an auxiliary array prePos in our pattern. prePos will hold integer values that will tell us the count of characters to be jumped. This supporting array can be described as the longest proper prefix that is also a suffix.

#programming #data-science #coding #kmp-algorithm #algorithms #algorithms

Beth  Nabimanya

Beth Nabimanya


Algorithm trading backtest and optimization examples

Algorithm trading backtest and optimization examples

Algorithmic trading backtests

Algorithm trading backtest and optimization examples.



#algorithms #optimization examples #algorithm trading backtest #algorithm #trading backtest