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Which programming languages are most suitable for the development of artificial intelligence? aiming at this problem, this article introduces the corresponding analysis and solution in detail, hoping to help more partners who want to solve this problem to find a more simple and feasible method.

In recent years, artificial intelligence is constantly releasing the huge energy accumulated by the scientific and technological revolution and industrial change, profoundly changing the human way of production and life and way of thinking, and promoting the overall jump of social productive forces. What is AI? What value will it bring to us? We continue to share with you a series of AI popular science articles. Please follow up on the follow-up updates!

The improvement of artificial intelligence technology not only brings efficiency to the operation of enterprises, but also brings convenience to people's lives. So far, artificial intelligence has realized projects such as biometric intelligence, self-driving cars and face recognition.

Like the development of most software applications, developers are writing artificial intelligence projects in multiple languages, but there is no perfect programming language that can fully match artificial intelligence projects.

The choice of programming language often depends on the desired functionality of artificial intelligence applications. The debate about the best artificial intelligence programming language never stops. Today we will share with you the five common programming languages that are most suitable for artificial intelligence development. Which one do you know?

01 、 Python

There is no doubt that first place is Python. Although some features of Python are flawed to varying degrees. But if you are working on AI, you are bound to use Python at some point. The number of libraries available in Python is unmatched by other languages.

As an extension library of Python, NumPy is becoming so important and universal that it has almost become the standard API of tensor operation. Based on NumPy,Pandas, R's powerful and flexible data frame can be brought into Python. For natural language processing, you can also use the famous NLTK and lightning-fast SpaCy.

For machine learning, there is a field-tested Scikit-learn. When it comes to deep learning, all current libraries are the first projects implemented on Python.

On LiveEdu, a German AI developer teaches you how to use Python to develop two simple machine learning models. )

Python is the frontier language of artificial intelligence research. It has the most machine learning and deep learning framework, and it is also a language mastered by almost all AI researchers at present.

02, JAVA and related languages

JVM series languages are also an excellent choice for AI application development.

Whether it's natural language processing, tensor computing, or the complete GPU accelerated deep learning stack, you can use a large number of libraries to manage all parts of the pipeline. In addition, you can easily access big data platforms such as ApacheSpark and ApacheHadoop.

Java is the lingua franca of most enterprises. Java8 and Java9 provide us with new language structures that make the experience of writing Java code not as bad as it used to be.

Writing artificial intelligence applications in Java may be a little boring, but it is a good helper for you to get the job done, and you can develop, deploy, and monitor based on all existing Java infrastructure.

03 、 C/C

Java C may not be your first choice when developing AI applications, but if you work in an embedded environment and can't afford the overhead of a Java virtual machine or Python interpreter, then Cmax C is your best choice.

You can choose the one that works best for you from the following methods:

Dive into the bottom of the stack and write your own code using libraries such as CUDA, which will run directly on GPU

You can also use TensorFlow or Caffe to access the flexible advanced API.

Of course, the latter also allows you to import models written by data scientists in Python and then run them in a production environment at a C-level speed.

04 、 Java

Java is a literal scripting language, a dynamically typed, weakly typed, prototype-based language with built-in support for types.

As we all know, Google released TensorFlow.js in 2018. TensorFlow.js is an open source hardware-accelerated Java library for training and deploying machine learning models.

TensorFlow.js is a WebGL acceleration library that allows you to train and run machine learning models in Web browsers. It also includes KerasAPI and the ability to load and use models trained in regular TensorFlow. This may attract a large number of JS developers to pour into the AI domain.

Although the machine learning library that Java can access is limited compared with other languages, in the near future, adding neural networks to web pages will be as simple as adding React components or CSS attributes.

In order to get the best computing performance in all kinds of environments, Tensorflow.js has implemented three back-end computing environments: pure Java or WebGL client and Nodejs server. At the same time, Tensorflow.js also supports the retraining of existing models to help users customize models that meet their business needs.

05, R language

R is at the bottom of the list and looks set to decline. R is a programming language preferred by data scientists. However, other programmers are a little confused when they first come into contact with R because it takes a data frame-centric approach.

If you have a group of specialized R developers, it makes sense and value to use R with TensorFlow, Keras, or H2O for research, prototyping, and experimentation.

However, based on performance and operational considerations, R is not recommended for production.

Although you can write high-performance R code that can be deployed on a production server, it should be easier to re-code this prototype written in R as Java or Python.

Language is the basic condition for dialogue with artificial intelligence. If we want to control artificial intelligence, we need to continue to hone it. How many of the five programming languages have you mastered? If you don't want to be illiterate in the future, learn and act quickly!

The answers to the questions about the programming language most suitable for the development of artificial intelligence are shared here. I hope the above content can be of some help to you. If you still have a lot of doubts to be solved, you can follow the industry information channel for more related knowledge.

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