Can I use C++ for machine learning?

Many developers are gradually getting into the development of AI applications. From a sophisticated program to another sophisticated program, we can spot in the background the repeated use of programming languages ​​such as Python, Java (and its brothers Scala, Kotlin, Clojure), C / C ++, JavaScript, or the language A. Beyond these 5 popular programming languages ​​for AI, there are other languages ​​like Lua, Julia, and Swift.

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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