#computer language

In 1989, Guido van Rossum, a Dutch computer scientist, started working on a new programming language that would be easy to learn and use. He named it after Monty Python's Flying Circus, his favorite TV show.

The language quickly gained popularity and a cult following among developers who loved its simplicity and ease of use. Python soon became the language of choice for scientific and academic communities.

As Python's popularity grew, it faced some significant hurdles. In the early days, critics panned the language for being slow and inefficient compared to other programming languages. However, the Python community was undeterred and developed several optimization techniques and libraries to improve performance.

In 2000, Guido van Rossum released Python 2.0, which introduced a number of major new features and improvements, including list comprehensions, a garbage collector, and support for Unicode. This version of the language solidified its position as a powerful and versatile programming language.

Over time, Python faced some new challenges. The release of Python 3.0 in 2008 introduced significant changes that caused compatibility issues with earlier versions of the language. However, the Python community rallied to provide tools and resources to help developers transition to the new version.

Despite these challenges, Python has continued to grow and evolve. Today, it is one of the most popular programming languages in the world, with a vast ecosystem of libraries and tools. Python is used for everything from web development and data analysis to scientific computing and artificial intelligence.

Cosine similarity is a measure of checking the similarity between two documents, texts, strings, etc.

It does so by representing the query as vectors in n-dimensional space. It then measures the angle between these vectors and gives the similarity based on the cosine of this angle.

If the queries are completely similar the angle will be zero; Thus the cosine similarity will be: > cos(angle_between_the _vectors)=cos(0)= 1

If the queries are completely dissimilar the vectors will be perpendicular; Thus the cosine similarity will be: > cos(angle_between_the _vectors)=cos(90)= 0

If the queries are completely opposite the vectors will be opposite to each other; Thus the cosine similarity will be: > cos(angle_between_the _vectors)=cos(180)= -1

The cosine similarity, mathematically, is given by:

Let's see an example:

Doc1 = "this is the first document" Doc2 = "this document is second in this order"

Vector representation of these documents: Doc1 = [1,1,1,0,1,1,0,0] Doc2 = [1,0,1,1,1,2,1,1]

ΣAiBi = (1*1)+(1*0)+(0*1)+(1*1)+(0*1)+(0*1)+(1*0)+(1*2) = 4 √(ΣAi)^2 = √(1+1+0+1+0+0+1+1) = √5 √(ΣBi)^2 = √(1+0+1+1+1+1+0+4) = √9

Cosine similarity = 4/(√5*√9) = 0.59

If you think of learning code like learning English, here are the “vocabularies” for you to describe layout to computers.

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Artificial Intelligence To be able to handle and understand digital technologies in a self-determined way requires a certain amount of code literacy. This term describes a basic knowledge of computers, the Internet and algorithms. However, Code Literacy is not only an important prerequisite for the responsible use of the Net, but also protects against digitalism - the belief in technology as a panacea, for example in the form of digitization. Here it is important to replace faith with knowledge through knowledge. The machine thinks, man steers Looking at sober facts about digitization and artificial intelligence, one thing becomes clear: neither is digitization the magic formula for solving all the problems plaguing modern civilization, nor is artificial intelligence the logical next stage of human evolution. Even though machines can think in the meantime, they are still guided by humans in these thinking processes. And even decisive machines modeled after man are still a long way off, because who does not understand the original, can not make a copy of it.