Codelybrary: December 2019

Tuesday, December 31, 2019

Top 10 Algorithms for Competitive Programming

A very happy new year 2020!
As I enter the final year of my MCA programme, I would like to share the following link which is very useful for Competitive Programming and programming is general.

https://www.geeksforgeeks.org/top-algorithms-and-data-structures-for-competitive-programming/#algo1

Monday, December 30, 2019

Worm code in Python

https://github.com/keniel123/Worm

Saturday, December 28, 2019

PDF: probability density function

https://en.m.wikipedia.org/wiki/Probability_density_function

Decision Surface/Boundary problem in Statistical Classification

https://en.m.wikipedia.org/wiki/Decision_boundary

In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known. Examples are assigning a given email to the "spam" or "non-spam" class, and assigning a diagnosis to a given patient based on observed characteristics of the patient (sex, blood pressure, presence or absence of certain symptoms, etc.). Classification is an example of pattern recognition.

In the terminology of machine learning,^[1]classification is considered an instance of supervised learning, i.e., learning where a training set of correctly identified observations is available. The corresponding unsupervised procedure is known as clustering, and involves grouping data into categories based on some measure of inherent similarity or distance.

Often, the individual observations are analyzed into a set of quantifiable properties, known variously as explanatory variables or features. These properties may variously be categorical (e.g. "A", "B", "AB" or "O", for blood type), ordinal (e.g. "large", "medium" or "small"), integer-valued (e.g. the number of occurrences of a particular word in an email) or real-valued (e.g. a measurement of blood pressure). Other classifiers work by comparing observations to previous observations by means of a similarity or distance function.

An algorithm that implements classification, especially in a concrete implementation, is known as a classifier. The term "classifier" sometimes also refers to the mathematical function, implemented by a classification algorithm, that maps input data to a category.

Terminology across fields is quite varied. In statistics, where classification is often done with logistic regression or a similar procedure, the properties of observations are termed explanatory variables (or independent variables, regressors, etc.), and the categories to be predicted are known as outcomes, which are considered to be possible values of the dependent variable. In machine learning, the observations are often known as instances, the explanatory variables are termed features (grouped into a feature vector), and the possible categories to be predicted are classes. Other fields may use different terminology: e.g. in community ecology, the term "classification" normally refers to cluster analysis, i.e., a type of unsupervised learning, rather than the supervised learning described in this article.

Relation to other problems

Frequentist procedures

Bayesian procedures

Binary and multiclass classification

Feature vectors

Linear classifiers

Friday, December 27, 2019

Windows 2000 operating system

https://simple.m.wikipedia.org/wiki/Windows_2000

Thursday, December 26, 2019

Network PROTOCOLS

Classification of compter networks based on PROTOCOLS used/supported by them

Wednesday, December 18, 2019

Cash Flow Statement: MCS035 (AFM)

Preparation of SCWC( Statement of Changes in WC a great illustration with explanation by
Kaursewise Tutorials

CFS great illustration by Kaursewise Tutorials.

Cash Flow Statement explained in a very simple and effective way by Ruchi Thakkar.
Nice explanation of what you understand by CFS

I am also active at: