Skip to main content

Intro to Generative Adversarial Networks | GANs 001

   GANs consist of three terms Generative Adversarial Network. Let's understand these three terms first. Generative : A Generative Model takes input training sample from some distribution and learns to represents that distribution. Adversarial : It basically means Conflicting or Opposing. Networks : These are basically neural networks. So,Generative Adversarial Networks are deep neural network architecture comprising of two neural networks compete with each other to make a generative model. A GAN consist of two class models : Discriminative Model :- It is the one that discriminate between two different classes of data.It tries to identify real data from fakes created by the generator Generative Model :- The Generator turns noise into an imitation of the data to try to trick the discriminator Mathematically, A Generative Model 'G' to be trained on training data 'X' sampled from some true distribution 'D' is the one which, given some standard random distrib...

Most Difficult Programming Languages | 2020

Today we're going to look at the five most difficult programming languages to learn.

 


1. BrainFuck

Brainfuck is an esoteric programming language developed by Urban Mülle in 1993.The language consists of only eight simple commands and an instruction pointer. It is not intended for practical use, but to challenge and amuse programmers. Although completely Turing is complete, it is not intended for practical use, but to challenge and entertain programmers. Brainfuck just needs to split commands into microscopic measures.

2.Cow

The programming language was developed by Sean Heber in 2003 and is thus out of date. The idea behind the creation of COW was to play with various concepts that the maker had in mind. Surprisingly, the programming language was always intended to be complicated, and not only that, it wasn't meant to be used for any practical purpose at all. The Cow programming language was designed with the bovine in mind.As a result, all instructions are some variation on 'moo'.

3.Intercal

Intercal is an esoteric programming language that was created as a parody in 1972. It satirizes aspects of various programming languages at the time. Don Woods and James Lyon developed the programming language while they were students at Princeton University. You will also die trying to learn the programming language and dare to code it.

4.Malbolge

Malbolge was named after the eight circle of hell in Dante's Inferno,the Malebolge. It was specifically designed to be almost impossible to use, via a counter-intutive 'crazy opration',base-three arithmetic, and self-altering code.

5.Whitespace

This language was introduced on April fools day in 2003. The day it was introduced people thought it was a joke but it wasn't actually. you are allowed to use only spaces,tabs and linefeeds to write your codes There are no keywords, and you just need to use whitespaces, tabs, and linefeeds to write your code. If you write a character, it will be ignored by the compiler. Well, it may be the most encrypted programming language in history, since it can only be written by the developers.


Comments

Popular posts from this blog

Best Platforms to Improve Machine Learning Skills | 2020

 Machine learning is one of the most exciting techniques one has ever encountered.The field of study that gives computers the ability to learn without being explicitly programmed is machine learning.Their are platforms that can help you improve your Machine Learning skills. Today I've come up with the list of some of my favorite platforms.   Platforms to Improve Machine Learning Skills 1. Kaggle The online community of data scientists and machine learning practitioners is Kaggle, a subsidiary of Google LLC. Kaggle is the largest data science community in the world.Kaggle enables users in a web-based data-science environment to find and publish data sets, explore and build models, work with other data scientists and machine learning engineers, and enter competitions to solve challenges in data science.With it's free GPUs, high paying competitions,massive community , thousands on datasets and notebooks, this platform helps a lot. 2. Seedbank It was launched by  'TensorFlow...