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Top 5 Best Web Development Frameworks For Popular Programming Languages

Here are the top 5 best web development frameworks for popular programming languages, with the help of these you can develop rapid web applications.

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Top 5 Best Web Development Frameworks in 2019

In past years, Web Application Development has gone through a few kinds of evolution. It has become simpler and more flexible for web developers to create web applications. Frameworks are simplified the whole web application development process. There are different types of Web Development Frameworks are out there. The top 5 best web development frameworks for popular programming languages.

Top 5 Best Web Development Frameworks

The Top Best Web Development Frameworks are listed below

1. Django

Language: Python

Python Django

Django is a Python-related opensource web application development framework; Django Follows MVC architectural pattern.

This Framework Supports building rapid applications development and clean and comfortable syntax such as programmatic and logical design. Still, it is not ideally suitable for beginners; Django is a full-stack web application development Framework for experienced developers and programmers.

The software is developed and maintained by the Django Software Foundation. The main target of this Framework is To simplify the complexity and ease of coding means less code and rapid development.

Django was Initial release on 15 July 2005.

Different popular sites use Django Framework like Public Broadcasting Service, Pinterest, Bitbucket, Instagram, Mozilla Firefox, The Washington Times, Disqus, Bitbucket, Nextdoor, and many others.

For More Info:- https://www.djangoproject.com/

2. Angular JS

Language: Javascript

Javascript Angular js

Angular js is an open-source front-end web application development framework, and it is suitable for single-page web applications (SPAs) written in javascript developed and maintained by Google and its community.

It is based on the client-side model–view–controller (MVC) and Model View ViewModel (MVVM) or model-view-whatever (MV*) architectural Pattern. The initial release on October 20, 2010;
Angular (version 2 or more) is a complete rewrite of the Angular Js (version 1); it is Written in a typescript programming language. It contains all the features of Angular JS and also including some other features.

Angular recommends using TypeScript language instead of javascript because Typescript contains additional features. Javascript, Typescript is a superset of javascript language developed by Microsoft; it provides predefined classes and interfaces.

Different websites use Angular Framework like Netflix, The Guardian, PayPal, Upwork, Freelancer, Weather, JetBlue, Lego, iStock Photo, and many more.

For More Info:- https://angular.io/

Also Read: What is Object-oriented and Aspect-oriented Programming Model or Paradigm?

3. Laravel

Language: PHP

PHP Laravel

Laravel is the open-source (licensed under the terms of MIT License ) world’s most popular and widely used PHP framework for web app development, developed by Taylor Otwell, Written in PHP.

It is planned for the development applications based on model–view–controller (MVC) architectural pattern and based on Symfony.

The Laravel is the more advanced and powerful alternative to the CodeIgniter framework.
Laravel Framework uses the Model View Controller architectural pattern. Laravel is a Good choice for CMS, news articles or blog sites, social media websites, forums, etc.

There are several good reasons for this Framework to get the most popular web application development framework; the above reasons are enough to dominate.

Laravel contains various key features like Object-relation mapping (ORM), Templating, easily connected to different types of databases; the process of authenticating and caching is straightforward in this Framework; you can also create an Application Programming Interface (API) easily because of it an inbuilt feature and also simplifies the routing functionality.

The initial release of this Framework was in 2011, and the latest version of this Framework is 5.7 in 2018.

Different websites use the Laravel framework like one plus, in, builtwith.com, worldoo.com, laravel.io, worldwalking.org, Cachet, Video, Flarum, TimeGrid, October Cms, and many more.

For More Info:- https://laravel.com/

4. Spring

Language:- Java

Java Spring

The Spring Framework is an application framework for creating both Web and Desktop applications.

Spring is a lightweight and Open-source Framework for Developing Java EE Applications. Developed by Rod Johnson in 2002 and the latest version of the spring framework is 5.1 September 21, 2018; it supports model–view–controller (MVC) Architectural Pattern.

Spring Framework is a replacement for the Enterprise JavaBeans (EJB) Model.

Spring framework contains different modules that can provide a specific level of services.

  • Spring Core Container
  • Aspect-oriented programming
  • Authentication and authorization
  • Convention over configuration
  • Data access(It supports both SQL (RDBMS) and NoSQL databases)
  • Inversion of control (dependency injection)
  • Messaging
  • Model–view–controller
  • Remote access framework
  • Transaction management
  • Remote management
  • Testing

Different websites use the Spring framework like yatra.com,spring.io, michaels.com, edufect.com, delfi.ee,cw.no, and www. is.fi, mascus.com, and many more.

For More Info:- https://spring.io/

5. Ruby on Rails

Language:- Ruby

Ruby On Rails

Ruby on Rails, or you can be called Rails, is a server-side web application framework for creating dynamic web applications.

Ruby on Rails framework written in Ruby under MIT License, initially developed by David Heinemeier Hansson. It supports model–view–controller (MVC) Architectural Pattern. Rails can also support different patterns and models, including convention over configuration (CoC), don’t repeat yourself (DRY), the active record pattern, etc.

The Ruby on Rails Framework is exposed in the 2000s for web application development ingenious Features such as seamless database table creations, migrations, and scaffolding of views to enable rapid application development.

The initial release in the year 2005 and the latest version of this Framework were 5.2.2 in 2018

There are different types of websites are using Ruby on rails frameworks like Twitter, GitHub, CrunchBase, Groupon, Shopify, Square Urban directory, Zend Desk, and many more.

For More Info:- https://rubyonrails.org/

Depends on your project requirements, you have to choose your web development framework.

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Artificial Intelligence (AI)

The Difference Between AI and Machine Learning?

A lot of digital content discusses AI and ML by using them as synonyms, while the leading technology they focus on is supervised learning – a branch of ML, but more on that later.

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Will AI render Software Developers Obsolete

Do you ever read articles mentioning both Artificial Intelligence (AI) and Machine Learning (ML) and ask yourself how do these relate and how do they differ from each other? Don’t worry; you’re not alone in this struggle. Our global society is becoming increasingly digitised, and chatbots, speech processing devices and intelligent mobile app algorithms are all around us, so you better catch up with the trends. It is time to demystify the concepts of AI and ML and finally bring some clarity into the blurry picture.

From my practice working in a bespoke software development company, I hear and read about deep learning, neural networks and autonomous systems all the time. This article puts the main differences between those buzzwords in the spotlight and reveals what hides behind it all. Some companies only claim to use AI to market their products better, hoping that only true tech enthusiasts will notice the difference. While there are indeed lots of overlapping technologies behind AI and ML, here are the fundamental difference setting them apart:

1. The Core of AI and ML as Technologies

A lot of digital content discusses AI and ML by using them as synonyms, while the leading technology they focus on is supervised learning – a branch of ML, but more on that later. Artificial Intelligence is a subsector of Information Technology (IT) that focuses on developing machine algorithms that simulate natural human intelligence and automating them to behave in a way that exerts intelligence.

The main areas that concern AI is learning from experience and efficient problem-solving. As John McCarthy, the father of AI, once put it, AI mimics human intelligence but is not constrained by biologically observable methods.

Machine Learning, on the other hand, is a subset of AI that concentrates on developing computer algorithms that get machines closer to learning like actual humans. Here is a Venn diagram to make it simpler. The recognition of distinct patterns and pattern regularities and the subsequent derivation of suitable solutions are the tasks that this technology masters.

An algorithm needs to be fed existing databases to learn to recognise and follow the patterns for this to happen. The ML generates artificial knowledge derived from its previous experience. All the knowledge that is gained can be generalised and applied for solving other problems. This approach allows previously unknown data to be processed and used quickly.

2. Types of Artificial Intelligence

Generally speaking, there are two main types of AI: weak (narrow) and strong (general). Most of the AI-powered software solutions we come across are weak ones, and it is called like that because it can only perform several tasks well, meaning it has limited functionality.

The narrow type of AI can successfully handle simple problem-solving tasks after the appropriate training period. Some of the main areas where narrow AI can shine are text, speech or image recognition, navigation systems, streaming services etc.

The second type of AI – the strong one, is yet to be fully developed. It refers to programming machines to perform complete tasks, requiring general intelligence and human-like consciousness. These types of futuristic robots will be able to think autonomously without special training. They will most like show some level of self-awareness, while it is not expected that narrow AI will reach such a cognitive awareness state.

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3. Types of Machine Learning

While we wait for this general AI type to become more than a far-fetched concept, let’s discuss the main subdivisions of ML, shall we? There are three core ML types: Supervised Learning (SL), Unsupervised Learning (UL) and Reinforcement Learning (RL). SL is when software developers instruct the algorithm to learn something, e.g. differentiate between a snail and a turtle by designing training and data sets.

These include input values (e.g. object features) with labels and the desired outcome (e.g. proper classification). For example, supervised learning already helps automate X-ray readings, face recognition, malware detection or weather forecasting.

UL stands for using only input data without a previously defined goal or human supervision. For instance, ML experts use unlabeled dataset, such as animal pictures without the labels ‘’snail’’ and ‘’turtle’’ with the goal that the program makes meaning of the data and detects underlying structures on it own.

The last type of ML is reinforcement learning is based on the reward principle. It all starts with an initial state without any background information, and the program must perform an action, and the system receives negative or positive feedback. The process continues until the desired shape is reached.

4. Deep Learning and Neural Networks

When reading about AI, people often stumble upon two other buzzwords, causing a mild degree of confusion, so let’s also explore what deep learning and neural networks mean. First, to cast some light on the two concepts, I should mention that deep learning is a subfield of machine learning, and the most widely used method for deep learning is by utilising neural networks.

The neural network concept has its foundation in the human biological networks in the brain, which receive signals from nearby cells and decide whether this signal is important and if they should send it further. In the context of machines, the input, e.g. cat’s picture, plays the role of a signal, which is transmitted through different layers and to get to the output (result), which can be if the system decides to categorise the picture as a cat. Applied to more complex scenarios, deep learning using neural networks are behind self-driving cars or voice control systems.

5. What are the Use Cases of AI

I hope you now recognise the differences between AI and ML and that all the attention around these trends is well deserved. Once again, human beings prove that with the right knowledge, using the right tools and techniques, countless inspiring solutions can be developed in the future.

Some of the most promising business domains where AI is already improving core processes are supply chain management, automated quality control, self-driving vehicles, automated support processes (e.g. ticketing systems, chatbots etc.), predictive maintenance. Moreover, AI carries a huge potential to facilitate innovation in the R&D (Research and Development) sector as Big Data and Data Analytics become inseparable parts of obtaining powerful data insights and drive businesses forward.

Aleksandrina is a Content Author at Dreamix, a custom software development company, and is keen on innovative technological solutions with a positive influence on our world. Her teaching background, mixed with her interest in psychology, drives her to share knowledge. She is an avid reader and enthusiastic blogger, always looking for the next inspiration.

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