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A Guied to use react-i18next in your component

Intro

We use react-i18next for internationalization our app which is based on i18next.

What we did for config i18next

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Sync your Git Fork to the Original Repo

List the currently configured remote repositories

git remote -v

Result Befor set upstream:

origin https://github.com/[Your UserName]/[Your Fork].git (fetch)

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Git Basic Commands

Remove a remote

1) First check the name of your remotes

$ git remote -v

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image refrence: wpxpo

LOAD MORE in Next.js

//Nextjs App: pages/blog/index.js
import Blog from '../../components/post/Blog';
import allPosts from '../../data/posts.json';

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Add Google Analytics to a Next.js Website

import '../styles/globals.css';
import Layout from '../components/layout/index.js';
import { Fragment } from 'react';
import Script from 'next/script.js';

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Jitsi: Free-open-source video conferenc

About Jitsi

Jitsi Meet is a free and open-source video conferencing platform that allows for secure and encrypted online meetings and video calls.

Platforms

It supports web-based, peer-to-peer and scalable architecture, and is compatible with various platforms including Windows, macOS, Linux, iOS and Android.

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Free and Open Source Video Editors

Kdenlive

For features see here: Features

Blender

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Machine Learning Tasks and Models

This article represents some of the most common machine learning tasks that one may come across while trying to solve machine learning problems. Also listed is a set of machine learning methods that could be used to resolve these tasks. Please feel free to comment/suggest if I missed mentioning one or more important points.

Following are the key machine learning tasks briefed later in this article:

1. Regression
2. Classification
3. Clustering
4. Similarity matching

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Interpretation of probability

The frequentist interpretation and Bayesian interpretation of probability are two philosophical and mathematical frameworks for understanding probability. They differ in their assumptions about the nature of probability, the role of data and evidence, and the interpretation of results.

Frequentist

The frequentist interpretation of probability defines probability as the long-run relative frequency of an event occurring in a large number of independent repetitions of a random experiment.

It does not allow for subjective beliefs or uncertainty in a proposition, but instead defines probability in terms of the observed frequency of an event. The frequentist interpretation is often used in statistical inference, where probabilities are associated with the likelihood of obtaining a certain data sample given a particular hypothesis or model.

Bayesian

The Bayesian interpretation of probability defines probability as **a measure of subjective belief or uncertainty in a prop

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Chapter 1: Introduction

Interpretation of probability

The frequentist interpretation and Bayesian interpretation of probability are two philosophical and mathematical frameworks for understanding probability. They differ in their assumptions about the nature of probability, the role of data and evidence, and the interpretation of results.

Frequentist

The frequentist interpretation of probability defines probability as the long-run relative frequency of an event occurring in a large number of independent repetitions of a random experiment.

It does not allow for subjective beliefs or uncertainty in a proposition, but instead defines probability in terms of the observed frequency of an event. The frequentist interpretation is often used in statistical inference, where probabilities are associated with the likelihood of obtaining a certain data sample given a particular hypothesis or model.