InĪddition, the invariance of filter feature high-rank decomposition is used toĮvaluate model sensitivity to different semantic concepts. Intelligence (AS-XAI) framework, which utilizes transparent orthogonalĮmbedding semantic extraction spaces and row-centered principal componentĪnalysis (PCA) for global semantic interpretation of model decisions in theĪbsence of human interference, without additional computational costs. Self-supervised automatic semantic interpretable explainable artificial Which hinder their practical applications. However,it remainsĭifficult for existing methods to achieve the trade-off of the three keyĬriteria in interpretability, namely, reliability, causality, and usability, Subjects: Computer Vision and Pattern Recognition (cs.CV) Artificial Intelligence (cs.AI) Human-Computer Interaction (cs.HC) Information Retrieval (cs.IR) Machine Learning (cs.LG)Įxplainable artificial intelligence (XAI) aims to develop transparentĮxplanatory approaches for "black-box" deep learning models. We presented DTN data types and protocols to be used for data integration. The application layer (AL), and what those layers encompass and beyond. That comprises the physical network layer (PNL), the digital twin layer(DTL), ![]() This paper proposed a data-driven digital twin network architecture, The framework of the Industrial Internet of Things, data processing,Īnd digital twin network is taken into consideration in this article as a keyĪspect. In both directions and revealing information about the progression of a network A digital twin connects the real and digital worlds, exchanging data As a result, network operation and maintenance are becoming moreĭifficult. Things, and cloud computing technology as well as the advent of new network Size continue to grow as a result of the development of 5G, the Internet of (DT) technology to produce virtual twins of real things. With Introduction to Python, which takes under an hour to finish, you can write a guessing game application as you learn to create variables, decision constructs, and loops.Subjects: Networking and Internet Architecture (cs.NI)Ī new network named the "Digital Twin Network" (DTN) uses the "Digital Twin" With Getting Started with R, you can start writing basic R commands and learn how to install packages and import data sets. Coursera’s Guided Projects offer a hands-on introduction in under two hours without having to buy or download any software. Try both through Guided ProjectsĪnother great way to decide whether to learn R or Python is to try them both out. These are just a few options for getting started. Luckily, no matter which language you choose to pursue first, you’ll find a wide range of resources and materials to help you along the way. They’re also both appropriate for beginners with no previous coding experience. Python and R are both excellent languages for data. How to learn R or Python: Options to get started Python is a general-purpose language used for a much wider range of tasks than R. The same is true if your personal or professional interests extend beyond data and into programming, development, or other computer science fields. If, on the other hand, you’re interested in becoming a data scientist and working with big data, artificial intelligence, and deep learning algorithms, Python would be the better fit. ![]() If you’re passionate about the statistical calculation and data visualization portions of data analysis, R could be a good fit for you. Think about how learning a programming language fits in with your longer term career goals. Its robust ecosystem of statistical packages Performing non-statistical tasks, like web scraping, saving to databases, and running workflows Read more: What Is Python Used For? A Beginner’s Guide to Using PythonĬreating graphics and data visualizations You can use Python code for a wide variety of tasks, but three popular applications include: ![]() Python is a high-level, general-purpose programming language known for its intuitive syntax that mimics natural language. ![]() So which should you choose to learn (or learn first)?īefore we dig into the differences, here’s a broad overview of each language. Both can handle just about any data analysis task, and both are considered relatively easy languages to learn, especially for beginners. Python and R are both free, open-source languages that can run on Windows, macOS, and Linux. Data analysts use SQL (Structured Query Language) to communicate with databases, but when it comes to cleaning, manipulating, analyzing, and visualizing data, you’re looking at either Python or R. One of the most important skills for a data analyst is proficiency in a programming language.
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