In January 2019, a service for hosting IT projects and their joint development Github has published a ranking of the most popular programming languages used for machine learning(MO). The list is based on the number of repositories whose authors indicate that their applications use MO algorithms.
The authors present the complete guide to ANSI standard C language programming. Written by the developers of C, this new version helps readers keep up with the finalized ANSI standard for C while showing how to take advantage of C’s rich set of operators, economy of expression, improved control flow, and data structures. The 2/E has been completely rewritten with additional examples and problem sets to clarify the implementation of difficult language constructs. For years, C programmers have let K&R guide them to building well-structured and efficient programs. Now this same help is available to those working with ANSI compilers. Includes detailed coverage of the C language plus the official C language reference manual for at-a-glance help with syntax notation, declarations, ANSI changes, scope rules, and the list goes on and on.
Python and C ++ are most often used to develop programs based on machine learning algorithms.
GitHub named Python as the most popular programming language among developers of MO programs in many ways for a set of pre-configured tools for implementing MO models and algorithms. Thanks to this, programmers can use Python to implement machine learning without deep knowledge in it and creating, for example,chat bots from scratch.
This became possible after the release of the well-documented Scikit-Learn library, which provides a large number of machine learning algorithms. Also noted is the presence of the ChatterBot library, designed for speech processing and training on datasets in dialog format.
2. C ++
C ++ ranked second among the programming languages used by GitHub users for machine learning. The high position is due to the creation of the MO library Google TensorFlow with emphasis on neural networks… Although the majority of developers and researchers who use TensorFlow work in Python, it is sometimes necessary to abandon this scheme. For example, when you need to use a trained model in a mobile application, or robot…
In addition, the popularity of C ++ on GitHub is due to the development of a distributed high-performance gradient boosting platform. Microsoft LightGBM (increases the speed and efficiency of ML model learning) and Turi Create libraries (simplifies the development of custom machine learning models for novice developers).
Such a popular project as Smile (Statistical Machine Intelligence and Learning Engine) was created in Java. It is a fast, comprehensive system designed to implement machine learning, NLP, linear algebra, graph, interpolation and visualization in Java and Scala.
Another popular GitHub repository where code is written in Java is H20. This machine learning library is designed for both local computing and using clusters created directly using H2O or working on a cluster Spark and Hadoop…
With access to libraries, it is not that hard to develop ML-based programs in any programming language
One of the most popular ML projects written in C # on GitHub is ML Agents. This open source plug-in for the Unity game engine allows games and simulated spaces to act as training environments for intelligent agents.
The most popular projects here are MachineLearning.jl, MLKernels.jl, and LightML.jl.
In this programming language, it is worth noting the Dl-machine scripts designed to configure the GPU for computations using CUDA with deep learning libraries.
The R programming language is popular in ML projects due to its large community and data analysis libraries.
There are several repositories on GitHub to help popularize Scala. Among them is Microsoft Machine Learning for Apache Spark.
This book introduces the concepts of diverse programming languages for students who have already mastered basic programming in at least one language. It is suitable for use in an undergraduate course for computer science and computer engineering majors. It treats all the knowledge units in the area of programming languages that appear in the ACM’s Computer Science Curriculum 2008, and introduces the core units thoroughly. It gives programming exercises in three different language paradigms. Philosophically, it is in complete agreement with the ACM report.