Is C++ or C# better for game development?

When it comes to a comparison between C# and C ++ in game development, C# has powerful drawing ability, the scope of application is large, and the portability is good. It possesses strong data processing capabilities, suitable for writing system software. C# can provide you with three-dimensional, two-dimensional graphics and animation. It is suitable for a variety of operating systems, such as DOS, UNIX, Windows 98, Windows NT. 

But, the shortcomings of the C# language are mainly manifested in the encapsulation of data, which makes C# have a big flaw in data security. This is also a big difference between C# and C++. The grammatical restrictions of the C# language are not too strict, and the type constraints of variables are not strict, which affects the safety of the game, and does not check the array subscripts beyond the boundary.

From the application point of view, the C# language is more difficult to master than other high-level languages, so it takes a lot of time to get familiar with it and then hit the purpose of developing related games.

A game engine involves a huge amount of content, and it needs to be efficient, high-performance, effective, and easy to expand. The high-performance key core code will be implemented using assembly, such as SIMD instructions for various floating-point mathematical operations, and high development efficiency, such as the tool layer, will be implemented using scripts. Only an all-around language like C++ can bridge the gap between assembly and scripting languages. In other words, C++ is responsible for the most important architectural part of the game engine.

The development efficiency of C++ is definitely higher than that of C, and even if the performance is good with C, it is not as good as 3 to 4 times that of C++, and 10 to 30% is good.

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Extremely optimized code, C is better than C++, but the performance of C++ code developed at the same time will be better than C, and the readability and maintainability of C++ is much better than C. There is really nothing that cannot be done with C. But C++ is better.

Why is C++ good for game development?

No complex program (and a video game with realistic graphics is the most complex there is in terms of quantity of operations) is written entirely in one and only language. The question is actually based on a simplistic statement: C ++ compiles directly into machine code, and compilers have various optimization methods that can make the code particularly fast.

This makes it particularly suitable for the development of “graphics engines,” which are part of a video game that performs the most complex and fastest operations. What then happens within the game (mapping between history and events) and the behavior of the various objects with respect to that of the player can also be modeled with different languages. 

A game engine involves a huge amount of content, and it needs to be efficient, high-performance, effective, and easy to expand. The high-performance key core code will be implemented using assembly, such as SIMD instructions for various floating-point mathematical operations, and high development efficiency, such as the tool layer, will be implemented using scripts. Only an all-around language like C++ can bridge the gap between assembly and scripting languages. 

In other words, C++ is responsible for the most important architectural part of the game engine. This part contains the most important engineering organization, from the underlying base library: extended std/boost data structure, packaging time, IO/file system, multithreading, reflection, memory management, math library. . . . To the middle layer: windows, game loops, input devices/messages, graphics rendering interface layer abstraction, real-time graphics rendering pipeline design, physics/collision detection, path-finding, bones, actions, models. . . . 

Then to the top level of the universal world space hierarchy, skybox, vegetation, water, particle special effects, camera, etc. The development efficiency of C++ is definitely higher than that of C, which is twice as high as there is no pressure, so you can use the saved development time to do performance optimization.

Is C++ the best language for games?

The way of creating video games has changed a lot in recent years. What once took hours now can be implemented in a simple way with suitable programming languages. Among different programming languages, C++ is the most used language in the games market, accounting for most applications created in this sector. Its bases are very close to C, which makes learning both languages ​​very important. With an object-oriented format, its characteristics are high performance and flexibility, which gives the programmer more freedom and makes it adaptable to different needs.

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C ++ was released in 1985, and today it is one of the most important programming languages ​​and one of the best. It is a highly optimized code that allows greater memory management, a necessary attribute in the development of high-definition video games with a large number of polygons. C ++ is a popular language for triple-A (AAA) titles, as well as independent games. The C ++ language runs with most game engines, such as Unity or Unreal, making it one of the most common selections for programmers. 

This language has a very low execution time, allows developers to have control over hardware, and is also compatible with most programming environments. Its learning curve is quite short and, if you master it, you can easily program in other languages. It is very popular because it can be run on almost any device, including complex video game systems. It is a fairly dynamic language with a variety of applications. Some games created under the Java programming language include Star Wars Galaxies, Runescape, Roboforge, and Powder Game.

It is one of the languages ​​considered as a fundamental pillar of web development and one of those mainly used to develop games on the Android operating system due to the growth of open-source codes. It is an excellent option for creating games that work on both browsers and mobile devices.

Is C++ bad for games?

And what is so bad and difficult about C++? Many would say that memory management is what makes C++ so difficult, but I don’t think so, for there are some techniques in C++ like correct use of constructors and destructors (aka, bizarrely, RAII) allocating your memory on the stack whenever you can, if you need to use the heap, using unique_ptr (C++11) is very effective. 

A feature that tends to get people off the hook when using C++ is its lack of a package system like node.js’ npm or even Java’s Maven; having to maintain header files is also extremely unproductive and the poor and confusing handling of strings. To understand what I mean by strings, take a look at the top-rated StackOverflow answer to a question about splitting a string.

istringstream iss(sentence);

copy(istream_iterator(iss),

 istream_iterator(), 

ostream_iterator(cout, “\n”));

In my opinion, this is too much suffering to do such a common thing. Compare with Java or Javascript:                     

str.split(“…”);

C++ has great strength, which is its strong use in game development; approximately 95% of commercial games use it, therefore having great acceptance and documentation about it. Its downside is that it doesn’t have as rich a built-in library as Java and Python, a disadvantage that is reversed by its benefits already mentioned and by the great availability of libraries to make up for this deficiency, which makes it, for the time being, sovereign in his reign. 

With all the above-mentioned pros, there are some cons of using C ++ for game development as well. This language is so complicated that “no one fully understands C++” is a popular joke. Basically, you will get (almost) all possible tools, and you will decide for yourself how to use them effectively.

This has led to different coding standards, different methods, different practices, restricting companies to C++ subset companies, etc.

Language requires more training and higher discipline. If you stick to certain practices, you will reap huge rewards, but you will need more practice to achieve your goals.

On larger codebases, the creation cycle of “compile → check for errors” is also lengthier. Again, you’d better use that moment to collect thoughts and things.

How is C++ used in game development?

Game programming is part of game development and is the process of programming a computer game. Computer games are developed for a wide variety of platforms such as PCs, game consoles, or smartphones. The most commonly used programming language is C or C ++, as the main focus when developing games is on speed. Above all, demanding games require the most direct and efficient access possible to the hardware of the system; this applies in particular to the graphics hardware. 

Special APIs have been developed for this purpose for the PC and now also for other platforms. The two top known for accessing graphics hardware is DirectX and OpenGL. Since these APIs are limited to the bare fundamentals, many program libraries have emerged over time, such as B. Allegro, SDL, SFML, which are based on DirectX or OpenGL.

Video games are a combination of various artistic media, including text, music, digital art, and animation, by tying them together as codes. For those who want to learn more about how to make a game, it can be confusing where to start.

The good news is that anyone with a PC can become a game maker these days. Game development tools have become readily accessible. Many development platforms are free to download and do not require deep knowledge of programming languages ​​or coding experience.

If you’re a passionate game developer, creating games is a lot easier than you originally thought. Some game developers start learning development by modifying their favorite games, but today’s game engines are so powerful that even beginners can use them to create games from scratch.

Games made with C++

There is a lengthy list. The main coding languages, however, are C++, C#, and old reliable HTML5.

C++ is a language with a comparatively high entry barrier (meaning it’s quite problematic to learn), but the return is greater because it permits more direct control over the hardware and graphical processes (something very significant in video game design). It is an object-oriented language, meaning it uses internal structures to well organize code into reusable blocks (classes and objects).

It is by far the most typically used language for writing game engines, and some engines (such as Unreal) only accept information written in C++. If you only choose one language to learn, this would be it because most other object-oriented languages stem from C++ (so picking them up will be easier).

The following games are made with C++:

1. Counter-Strike
2. Doom III Engine
3. King Quest
4. Starcraft
5. World of Warcraft
6. Warcraft III
7. Football Pro
8. Master of Orion III
9. Maplestory
10. Invictus

Best languages ​​for game development 2021 

Although there are hundreds of programming languages you can use if you want to develop a career plan and work in the virtual reality game, you must choose one of these five: 

• C ++ language has a very low execution time, allows developers to have control over hardware, and is also compatible with most programming environments. Its learning curve is quite short and, if you master it, you can easily program in other languages.   
• C #, although it is easier to learn than C ++, it is also more limited. However, despite not being as flexible or compatible, some engines like Unity allow programming with it. The fact master C # can open you many doors in the field professional since it is one of the languages most used in environments of Windows.  
• Java is characterized by being multiplatform and having enormous flexibility. Its usefulness and its design are like are to C ++, but, unlike this language, Java running inside your virtual machine, which means a loss of performance when running games.

• JavaScript. As it is one of the most widely used programming languages ​​in the field of web development, it presents a wide variety of resources. Most game engines are compatible with JavaScript, so, if mastered, they can use it to create all kinds of scripts. It works very well for online games and has multiple frameworks for creating 3D programs.  

• Python is a very powerful and flexible multiplatform programming language. One of the main benefits it offers is its Pygame framework, which allows you to create video game prototypes quickly and easily.  

You must bear in mind that there is no best language to program video games. The important thing is to know how to choose the most suitable tool for each occasion, taking into account your abilities and the specific needs of each project.  

Games made with C++

Game programming is the core and skeletal framework for all games you and I play. All of the crazy things you can do in the Grand Theft Auto games? All programmed.

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How is programming used in games?

Well, it takes a team of brilliant game programmers to add detailed features of games, along with the finished base product. Programming uses code to make the game a living, breathing thing. Programmers turn lines of code into fully functional and, sometimes, classic games.

Best programming languages for android games 

If you want to develop an Android application, the first step is to choose a language. The differences between the various Android programming languages ​​may be somewhat complicated and subtle. But more important than the language is the tool attached to it, as well as the main function and goal of the tool.

Android is the most widely used operating system for mobile devices, its presence is not only reduced to smartphones, but we have it in watches (Smartwatch), televisions, or other devices (wearables).

Android is based on a Java virtual machine. Therefore, it is to be assumed that its main language is Java, although now Google is betting a lot on another new language, Kotlin.

Java is one of the most popular programming languages ​​of the last 15 years. It is a multiplatform language; its simplicity and robustness when creating applications make it one of the most powerful languages ​​today. The big problem that Java has, if it can be called a problem is that it needs a virtual machine to be able to run, which can lead to performance problems.

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Kotlin is a really neophyte programming language since its date dates back to 2017, which was when Google announced support for its Android Studio. This language is inspired by Java and C ++, so it can be thought of as an improved version of these languages. Kotlin is a very clean and relatively simple language with far fewer formalities and rules than its predecessors would be. Its popularity has been growing, and currently, more than 50% of professional Android developers use this language for their applications.

It is not surprising, given the above, that Kotlin was the language that grew the most during 2018.

You must bear in mind that there is no best language to program video games. The important thing is to know how to choose the most suitable tool for each occasion, taking into account your abilities and the specific needs of each project. 

Game development is a relatively broad topic. It is not appropriate to say which language is better. It should be that language is more suitable for that level of processing. When developing games with a relatively large amount of data, the programming language C++ is undoubtedly more dominant and is a good choice for game development since it has already been used in widely some known projects.

Among different programming languages, C++ is the most used language in the games market, accounting for most applications created in this sector. Its bases are very close to C, which makes learning both languages ​​very important. With an object-oriented format, its characteristics are high performance and flexibility, which gives the programmer more freedom and makes it adaptable to different needs. C++ is a language with a comparatively high entry barrier (meaning it’s quite problematic to learn), but the return is greater because it permits more direct control over the hardware and graphical processes (something very significant in video game design). It is an object-oriented language, meaning it uses internal structures to well organize code into reusable blocks (classes and objects).

It is by far the most typically used language for writing game engines, and some engines (such as Unreal) only accept information written in C++. If you only choose one language to learn, this would be it because most other object-oriented languages stem from C++ (so picking them up will be easier).

REFERENCES

https://stackoverflow.com/questions/2203093/should-c-sharp-or-c-be-chosen-for-learning-games-programming-consoles

https://www.gamedesigning.org/career/programming-languages/

https://www.programmersought.com/article/16944397207/

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Choosing C / C ++ is unlikely when developing an AI application. But for those who work in an integrated environment and can’t afford the extra cost of a JVM or Python interpreter, C / C ++ is the way to go. When you need to exploit the latest system performance, you have to go back to the (terrifying to some) world of pointers. But, modern C / C ++ is nice because it offers multiple approaches. Developers can either dive to the bottom of the stack, using libraries like CUDA to write their program that runs directly on the GPU, or they can use TensorFlow or Caffe to access flexible high-level APIs.

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People recommend using Python while learning ML, not C++, and here’s the summary:

• Python is a more flexible language than C++.
• Library functions make the task trivially simple.
• To work interactively with the data, just run the Python interpreter from the command line.
• The main goal is to learn about ML, not practice programming.
• Debugging C++ code for ML algorithms is very difficult.
• C++ is more efficient than most other languages.
• You can control each of the resources starting with memory, CPU, and many other things.
• Most structures are implemented in C++ under the hood, such as TensorFlow, Caffe, vowpal, wabbit, and libsvm.
• Machine learning in C++ makes you a very desirable hiring target.

It is a good option to develop an application in AI. C/C++ is not always chosen, but it becomes an interesting alternative when the environment is integrated and cannot handle the overhead of the Java Virtual Machine or Python interpreter.

It’s easy to write with the C/C++ programming language. To help, the programmer has the possibility to use the libraries. CUDA helps design the code itself, while Caffe and TensorFlow provide access to high-level APIs. The last two libraries also let you import models built with Python and run them with speed. By all characteristics, this language is used to create neural networks and AI for games.

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Is Python or C++ better for machine learning?

Artificial Intelligence (AI) has already invaded the corporate world. If it used to be a trend, it is now a reality — with the expectation of having an increasingly wide use in the coming years. Therefore, it is essential that managers and IT have expert systems. However, these options are already outdated. Now the trend is the implementation of AI in commercial environments, which demand a simple and user-friendly interface.

Let’s see between C++ and Python, which is the best for machine learning:

Python: It’s one of the most common options when it comes to AI. The reason? The simplicity of using this programming language. Another benefit verified by programmers is the creation of prototypes. This process is very fast also because ideas are validated with just 30 or 40 lines of code. 

For these reasons, it’s virtually impossible to work with AI and leave that language out for the duration of development. On the other hand, Python presents some challenges. One of them is the massive division that occurs between its versions, the 2.x and the 3.x. Another obstacle is related to the five packaging systems, which act in different ways.

Python’s highlight is its libraries, practically unique. One of the most common is NumPy. Because it is so widely used, it has become a standard API for tensor operations. Pandas, on the other hand, presents R data frames. Another feature is the existence of libraries:

• NLTK and SpaCy for natural language processing;
• TensorFlow, Chainer, PyTorch, Theano, Apache MXNet, etc., for deep learning.

It’s important to note that Python is at the top when it comes to AI. It is in this language that the programmer finds much of the deep learning and the structures of Machine Learning. Therefore, it becomes indispensable even for recognized companies such as NASA, Google, IBM, and Embratel.

C/C++: It is a good option to develop an application in AI. It is not always chosen, but it becomes an interesting alternative when the environment is integrated and cannot handle the overhead of the Java Virtual Machine or Python interpreter.

It’s easy to write with the C/C++ programming language. To help, the programmer has the possibility to use the libraries. CUDA helps design the code itself, while Caffe and Tensor Flow provide access to high-level APIs.

The last two libraries also let you import models built with Python and run them with speed. By all characteristics, this language is used to create neural networks and AI for games.

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Why C++ is not used in machine learning?

The core algorithm of artificial intelligence is completely dependent on C/C++, but the upper-level logic is too inefficient to develop in C++. Python syntax is simple and rich, and the support for C is also very good. Although Python is slow, it just calls the AI ​​interface. The real calculations are all based on data written in C/C++. Using Python is just to write the corresponding logic, and a few lines of code will come out. 

Python is a general script development language, simpler and easier to learn than other programming languages, and its object-oriented features are even more thorough than Java, C#, .NET, and it is very suitable for rapid development. Python has advantages in software quality control, development efficiency, and portability. In terms of performance, component integration, library support, etc., they are all in an advanced position.

Python’s development ecology is mature, and there are many useful libraries available. In short, Python’s status as the language of choice for AI can no longer be shaken. The calculation using Python is very natural, simple, and efficient. Although Python is slow, it just calls the AI ​​interface. The real calculations are all written in C/C+ + at the bottom level. Python is just to write logic, that is, how to calculate the first step and how to calculate the second step, and a few lines of code will come out.

If you change to C++, you have to study it for 1 month before you can compile it. It is not that the upper-level logic cannot be written in C++, but the amount of code is too large, and the development efficiency is too low. In exchange, the overall speed is increased by 1%, which is inappropriate. Computer languages ​​have their own applicability; that is, C/C++ is fast and suitable for low-level writing algorithms, and Python is slow but suitable for upper-level writing logic. Because of these reasons, C++ is not used in machine learning.

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C++ machine learning projects

Caffe, Convolutional Architecture for Fast Feature Embedding, is an open-source project grounded in Artificial Intelligence and a deep learning framework created at BAIR (Berkeley AI research Center).

The key code or the code which would be accountable for running the entire show is actually written in C++ and then served as a Python library. Caffe is a tool that was formed with computer vision in mind. Mostly because of the speed, the fact that it is modular, and its overall expressiveness. Now we can simply see Caffe being used in large-scale applications. The key set of features that makes it stand out from the others are listed below: 

a. The overall design of Caffe was formed, keeping its expressiveness in mind. Because of the nature of this project, you can use it to mix out a substantial amount of data. It also inspires developers to think outside of the box.

b. You have the power of swapping from a CPU to a GPU whenever you want without tanking your performance meaningfully. It is generally the case that all Artificial Intelligence projects run much better when they are used on a GPU instead of a CPU. 

c. If you happen to use a GPU, however, you would get better performance. 

d. Caffe is speedy. This brands it as one of the best tools to be used in research.

TensorFlow is formed by the team at Google and they made it so that anyone would be able to see what is happening under the hood.

TensorFlow has one of the most flexible architectures that we have seen in some time. You can shift between using CPUs and GPUs with the help of just a single API call. 

Like Caffe, even TensorFlow is powered under the hood by C++ and is enfolded in a Python layer and then served as a Python library. 

Some salient features make TensorFlow great. We have listed some of them below: 

a. If you are using TensorFlow, you get access to something which is known as a Tensor board. With the help of a tensor board, you can easily see how your model performs graphically. 

b. TensorFlow happens to be a very flexible library, which is also very modular. You have the power to choose the parts which you want to include if you are making it standalone. 

c. With the help of the LSTM (long short-term memory) model of TensorFlow, you would be able to generate responses to the emails without you having to do anything.

d. It uses something which is known as feature columns. It is used to work between the data, which is unprocessed, and the estimators, which can be used to send or receive signals to your model.

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Is C++ or Python better for AI?

In many software development areas, including scripting and process automation, website development, and general-purpose applications, Python is becoming more and more popular. And recently, Python has become the language of choice for machine learning and is considered better than C++. We will take a look at the four major reasons why Python has become the dominant player in this field.

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Python makes programming easier

If you inherit machine learning applications developed by other developers, especially those that combine many third-party components or have a large number of customized business logic, the fewer obstacles caused by the programming language in understanding the program the better. Python code can do this far better than other languages. Another important feature that Python’s language design is useful in machine learning is that it offers high-level, object-based task abstraction.

Python offers a codebase for machine learning

The second main reason why Python has become the main force of machine learning is that Python provides a large number of codebases and frameworks for machine learning. Headed by the veteran Scikit-learn, every great machine learning and deep learning framework, including TensorFlow, CNTK, Apache Spark MLlib, etc., either enjoy the first-level treatment in Python or contains Python API. 

Python can handle memory management

The abstractions provided by high-level languages ​​such as Python and the work that uses them extend to many other areas. In Python, programmers cannot see the details of memory management, so they can focus more on the problem at hand.

Python speed is not a problem

Many people describe Python like this: “It’s convenient, but not fast enough.” Most of this sentence is correct. When using Python, you generally have to give up part of the performance for easy development. So someone may ask if Python is not the fastest language, why we use it for computationally intensive tasks, such as machine learning.

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The simple answer is: computationally intensive work is not done by Python.

In machine learning applications written in Python, most of the actual computing work is performed by other code libraries. These code libraries are usually written in C, C++, or Java, and Python is just packaged and interacted with. The parts of the application that run in Python are usually not very sensitive to performance-they are generally responsible for installation or removal, command, and control, coordination of various components, logging and reporting, and so on.

However, if the application spends a lot of time back and forth between Python and other fast codebases, using external code bases in Python can still cause performance issues.

C++ or Java for machine learning

Java

Common in different works, Java is also used in AI. It is suitable for large-scale projects because it offers adequate support. In addition, it facilitates user interaction, visualization and data debugging.

Regarding libraries, Java also has different options, which cover the entire pipeline. For natural language processing, for example, you can use CoreNLP. For tensor operations, the ND4J. And for accelerated deep learning, the GPU (DL4J). One advantage is that it offers easy access to big data platforms such as Apache Hadoop and Apache Spark.

Due to the improvements in this programming language, especially with the builds offered in versions 8 and 9, Java has simplified code building and application writing. If you prefer, you can use this option for deployment and monitoring.

It’s worth knowing that Weka, one of the main tools for machine learning and data mining, was written in Java and is open source. What does that mean? You have the chance to access the source code of the available algorithms and integrate them with your application.

C/C++

It is a good option to develop an application in AI. It is not always chosen, but it becomes an interesting alternative when the environment is integrated and cannot handle the overhead of the Java Virtual Machine or Python interpreter.

Choosing C / C ++ is unlikely when developing an AI application. But for those who work in an integrated environment and can’t afford the extra cost of a JVM or Python interpreter, C / C ++ is the way to go. When you need to exploit the latest system performance, you have to go back to the (terrifying to some) world of pointers. But, modern C / C ++ is nice because it offers multiple approaches. Developers can either dive to the bottom of the stack, using libraries like CUDA to write their program that runs directly on the GPU, or they can use TensorFlow or Caffe to access flexible high-level APIs.

C++ AI library

Some of them are listed below:

TensorFlow 

TensorFlow is a widespread open-source software library for machine learning. This library has a complete, flexible ecosystem of tools, libraries, and community resources that lets researchers and developers build and install ML-powered applications easily.

Caffe 

Convolutional Architecture for Fast Feature Embedding or Caffe is a deep learning framework written in C++. The structures of this library include expressive architecture, extensible code, speed, and a large community that brings up active development in research and industry deployments.

Mlpack library

Mlpack is a fast and flexible machine learning library, written in C++, providing the most advanced machine learning algorithms with Python bindings, Julia bindings, and C++ classes.

DyNet

The Dynamic Neural Network Toolkit (supporting dynamic computing graphics) or DyNet is a high-performance neural network library written in C++ (with bindings in Python) that can run efficiently on the CPU or GPU. It supports natural language processing, graphic structure, reinforcement learning, etc.

OpenNN

Open Neural Network (OpenNN) is an open-source (C/C++) neural network high-performance library for advanced analysis, supporting classification, regression, prediction, etc.

SHARK library

Shark is a fast, modular, and general-purpose, open-source machine learning library (C/C++) for application and research, supporting linear and non-linear optimization, kernel-based learning algorithms, neural networks, and various other machine learning techniques.

Armadillo

Armadillo is a linear algebra (C/C++) library with functions similar to Matlab. The library is famous for quickly converting research codes into production environments, pattern recognition, computer vision, signal processing, bioinformatics, statistics, econometrics, etc.

Faisis

This library (C/C++) is used for efficient similarity search and dense vector clustering. It contains algorithms for searching in vector sets of any size, which may not fit into the vector sets in RAM. It also supports optional GPU and optional Python interface provided through CUDA.

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Conclusion

It is a good option to develop an application in AI. C/C++ is not always chosen, but it becomes an interesting alternative when the environment is integrated and cannot handle the overhead of the Java Virtual Machine or Python interpreter. Whereas Python is one of the most common options when it comes to AI because of the simplicity of using this programming language. Another benefit verified by programmers is the creation of prototypes. This process is very fast also because ideas are validated with just 30 or 40 lines of code. 

For these reasons, it’s virtually impossible to work with AI and leave that language out for the duration of development. On the other hand, Python presents some challenges. One of them is the massive division that occurs between its versions, the 2.x and the 3.x. Another obstacle is related to the five packaging systems, which act in different ways. Python’s development ecology is mature, and there are many useful libraries available. In short, Python’s status as the language of choice for AI can no longer be shaken. The calculation using Python is very natural, simple, and efficient. Although Python is slow, it just calls the AI ​​interface. The real calculations are all written in C/C+ + at the bottom level. Python is just to write logic, that is, how to calculate the first step and how to calculate the second step, and a few lines of code will come out.

If you change to C++, you have to study it for 1 month before you can compile it. It is not that the upper-level logic cannot be written in C++, but the amount of code is too large, and the development efficiency is too low. In exchange, the overall speed is increased by 1%, which is inappropriate. Computer languages ​​have their own applicability; that is, C/C++ is fast and suitable for low-level writing algorithms, and Python is slow but suitable for upper-level writing logic. Because of these reasons, C++ is not used in machine learning.

REFERENCES

https://en.wikipedia.org/wiki/Machine_learning

https://analyticsindiamag.com/top-10-libraries-in-c-c-for-machine-learning/

https://www.cuelogic.com/blog/role-of-python-in-artificial-intelligence#:~:text=Python%20is%20a%20more%20popular,a%2057%25%20vote%20among%20developers.&text=Performance%20wise%20C%2B%2B%20outperforms%20Python,compact%20and%20faster%20runtime%20code.

https://dev.to/evilprince2009/why-is-artificial-intelligence-driven-by-python-and-not-c-4e5f