44

Python comes with a huge amount of inbuilt libraries. Many of the libraries are for Artificial Intelligence and Machine Learning. Some of the libraries are TensorFlow (which is a high-level neural network library), scikit-learn (for data mining, data analysis and machine learning), pylearn2 (more flexible than scikit-learn), etc. The list keeps going and ...


31

Practically all of the most popular and widely used deep-learning frameworks are implemented in Python on the surface and C/C++ under the hood. I think the main reason is that Python is widely used in scientific and research communities, because it's easy to experiment with new ideas and code prototypes quickly in a language with minimal syntax like Python. ...


9

What attracts me to Python for my analysis work is the "full-stack" of tools that are available by virtue of being designed as a general purpose language vs. R as a domain specific language. The actual data analysis is only part of the story, and Python has rich tools and a clean full-featured language to get from the beginning to the end in a single ...


8

Python has a standard library in development, and a few for AI. It has an intuitive syntax, basic control flow, and data structures. It also supports interpretive run-time, without standard compiler languages. This makes Python especially useful for prototyping algorithms for AI.


4

It's a mix of many factors that together make it a very good option to develop cognitive systems. Quick development Rapid prototyping Friendly syntax with almost human-level readability Diverse standard library and multi-paradigm It can be used as a frontend for performant backends written in compiled languages such as C/C++. Existing performant numerical ...


3

Python has rich library, it is also object oriented, easy to program. It can be also used as frontend language. That's why it is used in artificial intelligence. Rather than AI it is also used in machine learning, soft computing, NLP programming and also used as web scripting or in Ethical hacking.


3

That’s because python is a modern scripting object-oriented programming language that has stylish syntax. Contrary to structural programming languages like java and C++, its scripting nature enables the programmer to test his/her hypothesis very fast. Furthermore, there are lots of open source machine learning libraries (including scikit-learn and Keras) ...


1

Nothing. Its in almost everyone's favor for it to stay that way financially. Having non-technical individuals associate AI with terminators makes a perception that the field has greater capabilities than it does $\rightarrow$ this leads to grants, funding, etc... Is there any negative? Yes. Misconceptions always have drawbacks. We see the creation of ...


1

Many people who are interested in machine learning aren't professional programmers. For example there are mathematicians who work on differential equations and there are physicists who work on stochastic processes. These people aren't programmers. So using a language like C++ which is hard to learn is only detrimental to their works. And also creating a ...


1

We also work with Python in our company. One of the sphere that we use it for is fast prototyping and building highly scalable web applications. For over two decades, our Python developers have been providing businesses with full-stack web-development services, client-server programming and administration. We help our clients build high-load web portals, ...


1

I actually prefer C for machine learning. Because like in life, in the world as we know it, consists of never-ending "logic gates" (which basically is like flipping a coin - there WILL be 2 possible outcomes - not counting the third: landing on the side!). Which also means that while the universe seems never-ending, we still never stop finding those things ...


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