[-AI-3.5]Ten machine learning applications that you need to know on Linux system

Abstract

Linuxは機械学習に適した優れた環境を提供します。Pythonをはじめとする様々なツールやライブラリが利用可能であり、モデル作成からデプロイまでの全プロセスを実行することができます。この記事では、初心者でも簡単に理解できるように、Linuxで使用できる10個の機械学習アプリケーションをシンプルかつ簡潔に紹介しています。

機械学習とは、人間の介入なしにデータから学習し、予測や意思決定を行うプログラムを構築する分野です。機械学習アルゴリズムには、教師あり学習、教師なし学習、強化学習などがあります。

Pythonは、機械学習アプリケーションを開発するためによく使用されるプログラミング言語の1つです。Pythonのバージョンの選択は、アプリケーションの開発において非常に重要です。Python 2.xがまだ一部で使用されていますが、Python 3.xが広く推奨されています。変数の定義はPython開発において重要なステップであり、関数の使用は、JavaやC++など他の言語でも使われる基本的な機能です。

TensorFlowは、Googleが開発したオープンソースの機械学習フレームワークで、多くのニューラルネットワークを実装でき、自然言語処理、画像認識、音声認識などに適しています。Googleのクラウド機械学習エンジンでも使用されています。

Kerasは、ディープラーニングアルゴリズムを簡単に実装できる最も人気のあるPythonモジュールの1つです。Kerasは、コードの品質に重点を置いており、よりクリーンで簡潔なコードでモデルを構築できます。さらに、Kerasは学習率調整に関連する問題を解決するために設計されています。

Apache MXNetは、機械学習アルゴリズムを実装するために使用される分散型オープンソースの深層学習フレームワークで、Amazon Web Servicesによって開発されました。MXNetは、深層学習を使用してビジネスプロセスの自動化を実現するのに適しています。MXNetは、複数のマシンに事前コンパイルされたニューラルネットワークを配布できます。

Scikit-Learnは、Pythonのオープンソースの機械学習ライブラリで、簡単で強力なツールを提供します。Scikit-Learnは、数え切れないほどの機械学習アルゴリズムを実装します。このライブラリは、特徴選択、データ前処理、モデル選択、次元削減、クラスタリング、モデル構築など、広範な機能を提供します。

OpenCVは、画像処理や物体検出に使用されるコンピュータビジョンのライブラリです。OpenCVは、Python、C ++、Java、Matlabなど、多くのプログラミング言語と互換性があります。OpenCVを使用すると、画像処理やコンピュータビジョンに関連する多くの問題を解決できます。

CNTKは、Deep Learningに使用されるオープンソースツールキットで、Microsoft Cognitive Toolkitの略称です。CNTKは、他のフレームワークと比較してより効率的で高速な計算を提供していることで知られています。

Table of contents.

• Introduction
• What is Machine Learning?
• Basics of Python
• TensorFlow
• Keras
• Apache MXNet
• Scikit-learn
• OpenCV
• CNTK
• Conclusion

-- CAUTION

[Text]This blog is purely generated by Cohesive.ai

Introduction

Linux provides an excellent environment for machine learning. Various tools and libraries, including Python, are available, making it possible to execute the entire process, from model creation to deployment. This article introduces 10 machine learning applications that can be used on Linux, explained simply and concisely, even for beginners.

What is Machine Learning?

Machine learning is a field where programs learn and make predictions or decisions from data without human intervention. Machine learning algorithms include supervised learning, unsupervised learning, and reinforcement learning.

Python Basics.

Python is a language commonly used for developing machine learning applications. Choosing the version of Python is very important in application development. While Python 2.x is still being used in some cases, Python 3.x is widely recommended. Defining variables is an important step in Python development. Variables are containers used to store and manipulate data. The use of functions is a basic feature used in Python, as well as in other languages such as Java and C++. Using functions allows for easy execution of complex logic.

TensorFlow.

TensorFlow is an open-source machine learning framework developed by Google. It can implement many neural networks and is suitable for natural language processing, image recognition, speech recognition, and more. It is also used in Google’s Cloud Machine Learning Engine.

Keras

Keras is one of the most popular Python modules that makes it easy to implement deep learning algorithms. Keras places great emphasis on code quality, which allows building models with cleaner and more concise code. Furthermore, Keras is designed to solve problems related to adjusting learning rate. It is a machine learning framework that provides many tools as components and is an optimal choice for designing the first model, being a simple framework. However, Keras can also be used for many general-purpose tasks. Some of the practical applications of Keras include text classification, image recognition, speech recognition, natural language processing, recommendation systems, support for adversarial generative networks (GAN), and deep learning model support.

Apache MXNet.

Apache MXNet is an open-source distributed deep learning framework used for implementing machine learning algorithms. Developed by Amazon Web Services, MXNet is suitable for achieving business process automation using deep learning. MXNet can distribute pre-compiled neural networks across multiple machines.

The neural networks of MXNet are designed using NumPy to enable hardware- aware execution and optimisation. MXNet also supports multiple languages, including Python, C++, Scala, Matlab, and R. Applications of this framework include image recognition using convolutional neural networks and natural language processing using recurrent neural networks. Apache MXNet is known for its speed, performance, and versatility, making it widely used across different industries.

Scikit-learn.

Scikit-learn is an open-source machine learning library for Python that provides easy and powerful tools. Scikit-learn implements countless machine learning algorithms. This library offers a wide range of functionality including feature selection, data preprocessing, model selection, dimensionality reduction, clustering, and model construction.

Scikit-learn supports many features such as supervised and unsupervised learning, class classification, regression, clustering, module selection, optimization, etc. It is widely used in many industries and guarantees simpler implementation and higher accuracy. Applications of scikit-learn include medical diagnosis, image recognition, financial forecasting, customer segmentation, astronomy, and genetic analysis.

The readability of this library’s code is widely supported by beginners. This tool is one of Python’s green toolboxes and is a very attractive option for individuals who already have a good grasp of the programming language. Let’s embrace the beauty of scikit-learn!

OpenCV

OpenCV is a library for computer vision that is used for image processing and object detection. OpenCV is compatible with many programming languages such as Python, C++, Java, and MATLAB.

In applications, it is used for face recognition, object detection, image processing, and video analysis. Using OpenCV, it is possible to solve many problems related to image processing and computer vision.

CNTK(Microsoft Cognitive Toolkit)

CNTK is an abbreviation for Microsoft Cognitive Toolkit (formerly known as CNTK), an open-source toolkit used for deep learning. CNTK is known for providing more efficient and faster calculations compared to other frameworks. Applications of CNTK include speech recognition, image processing, natural language processing, and anomaly detection. CNTK also supports distributed training, which is why an increasing number of people are recognizing its use.

Conclusion

First of all, for those who want to use machine learning applications on Linux systems, this is a very helpful article. In this article, we will explain in detail about 10 machine learning applications. Let’s deepen our understanding of machine learning and gain knowledge to run it on Linux systems. So, let’s start reading it now.