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Littérature scientifique sur le sujet « JupyterLab »

Auteur : Grafiati

Publié le 2 juin 2025

Mis à jour le 31 juillet 2025

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Articles de revues sur le sujet "JupyterLab"

Erokhin, Andrey, and Andrey Zarochentsev. "JupyterLab at the Saint Petersburg State University." Journal of Physics: Conference Series 1525 (April 2020): 012062. http://dx.doi.org/10.1088/1742-6596/1525/1/012062.

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Erdmann, Martin, Benjamin Fischer, Lukas Geiger, et al. "Evolution of the VISPA-project." EPJ Web of Conferences 214 (2019): 05021. http://dx.doi.org/10.1051/epjconf/201921405021.

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VISPA (Visual Physics Analysis) is a web-platform that enables users to work on any secure shell (SSH) reachable resource using just their webbrowser. It is used successfully in research and education for HEP data analysis. The emerging JupyterLab is an ideal choice for a comprehensive, browser-based, and extensible work environment and we seek to unify it with the efforts of the VISPA-project. The primary objective is to provide the user with the freedom to access any external resources at their disposal, while maintaining a smooth integration of preconfigured ones including their access perm

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Thanuja Nishadi, A. S. "Text Analysis: Naïve Bayes Algorithm using Python JupyterLab." International Journal of Scientific and Research Publications (IJSRP) 9, no. 11 (2019): p9515. http://dx.doi.org/10.29322/ijsrp.9.11.2019.p9515.

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Cervera, Enric, and Angel P. Del Pobil. "ROSLab: Sharing ROS Code Interactively With Docker and JupyterLab." IEEE Robotics & Automation Magazine 26, no. 3 (2019): 64–69. http://dx.doi.org/10.1109/mra.2019.2916286.

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Niimura, Masaaki, Kazuki Kobayashi та Shinpei Ogata. "JupyterLab によるPython 学習のはじめかた". IEICE Communications Society Magazine 15, № 4 (2022): 333–38. http://dx.doi.org/10.1587/bplus.15.333.

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Cruz, Vanderson da Silva, Mateus Silva Rêgo, Iure da Silva Carvalho, et al. "ESTUDO TEÓRICO DA EQUAÇÃO DE SCHRODINGER PARA OSCILADORES: HARMÔNICO E ANARMÔNICOS QUÁRTICO, SÊXTUPLO E ÓCTUPLO QUÂNTICOS." REVISTA FOCO 17, no. 3 (2024): e3316. http://dx.doi.org/10.54751/revistafoco.v17n3-103.

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Este artigo explora o potencial da simulação computacional como uma ferramenta para superar limitações experimentais e teóricas na física, com um foco específico na mecânica quântica. O objetivo é resolver numericamente a equação de Schrodinger, utilizando o método de elementos finitos (MDF), para diversos potenciais, incluindo dos osciladores harmônico e anarmônico quártico, sêxtuplo e óctuplo. O estudo emprega o MDF, com apoio das plataformas JupyterLab e wxMaxima, para a diagonalização e discretização dos sistemas. São investigados osciladores harmônicos e anarmônicos, com resultados que in

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Zhou, Chenliang, Dominic Kuang, Jingru Liu, et al. "AISpace2: An Interactive Visualization Tool for Learning and Teaching Artificial Intelligence." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 09 (2020): 13436–43. http://dx.doi.org/10.1609/aaai.v34i09.7068.

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AIspace is a set of tools used to learn and teach fundamental AI algorithms. The original version of AIspace was written in Java. There was not a clean separation of the algorithms and visualization; it was too complicated for students to modify the underlying algorithms. Its next generation, AIspace2, is built on AIPython, open source Python code that is designed to be as close as possible to pseudocode. AISpace2, visualized in JupyterLab, keeps the simple Python code, and uses hooks in AIPython to allow visualization of the algorithms. This allows students to see and modify the high-level al

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Beer, Max, Niclas Eich, Martin Erdmann, et al. "Knowledge sharing on deep learning in physics research using VISPA." EPJ Web of Conferences 245 (2020): 05040. http://dx.doi.org/10.1051/epjconf/202024505040.

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The VISPA (VISual Physics Analysis) project provides a streamlined work environment for physics analyses and hands-on teaching experiences with a focus on deep learning. VISPA has already been successfully used in HEP analyses and teaching and is now being further developed into an interactive deep learning platform. One specific example is to meet knowledge sharing needs in deep learning by combining paper, code and data at a central place. Additionally the possibility to run it directly from the web browser is a key feature of this development. Any SSH reachable resource can be accessed via

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Fawzi, Ahmad, Joni Fat, and Meirista Wulandari. "Evaluation Method of Real-Time Face Detection." International Journal of Application on Sciences, Technology and Engineering 1, no. 2 (2023): 643–50. http://dx.doi.org/10.24912/ijaste.v1.i2.643-650.

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Object detection is one of computer technology using image or video. Face detection is closely related Image processing and computer vision are used to detect several objects including human faces, landscapes, cars, etc. Face detection algorithm aims to confirm if an image has a face as the object in it. In this study, face detection uses several methods, namely the Eigenface method, the Fisherface method, and the Local Binary Pattern Histogram (LBPH) method. This study used 10 different subjects. The test was carried out 15 times using each face detection method with constant distance. The fa

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Trujillo-Franco, Luis Gerardo, Julio Alexis Guízar-García, Jaime González-Sierra, Josué Daniel Rivera-Fernández, Hugo Francisco Abundis-Fong, and Erik López-Vázquez. "Jupyter notebook como auxiliar en la enseñanza de ecuaciones diferenciales con experimentos." Pädi Boletín Científico de Ciencias Básicas e Ingenierías del ICBI 12 (November 30, 2024): 120–27. https://doi.org/10.29057/icbi.v12iespecial4.13281.

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Las ecuaciones diferenciales ordinarias tienen una considerable presencia en las diversas ramas de la ingeniería, dada su utilidad en el modelado y descripción de los fenómenos y procesos con los que el ingeniero tiene contacto en su quehacer creativo y de solución de problemas. Es por esta razón que, en los programas de estudio enfocados a la ingeniería, existe al menos un curso dedicado al tema de las ecuaciones diferenciales, donde se abordan los métodos analíticos y numéricos utilizados para su solución. Hoy en día existen muchas herramientas tecnológicas que permiten ilustrar de manera mu

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Thèses sur le sujet "JupyterLab"

Zhang, Ji. "JupyterLab_Voyager: A Data Visualization Enhancement in JupyterLab." DigitalCommons@CalPoly, 2018. https://digitalcommons.calpoly.edu/theses/1849.

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With the emergence of big data, scientific data analysis and visualization (DAV) tools are critical components of the data science software ecosystem; the usability of these tools is becoming extremely important to facilitate next-generation scientific discoveries. JupyterLab has been considered as one of the best polyglot, web-based, open-source data science tools. As the next phase of extensible interface for the classic iPython Notebooks, this tool supports interactive data science and scientific computing across multiple programming languages with great performances. Despite these advantag

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Livres sur le sujet "JupyterLab"

Toomey, Dan. Learning Jupyter 5: Explore interactive computing using Python, Java, JavaScript, R, Julia, and JupyterLab, 2nd Edition. Packt Publishing - ebooks Account, 2018.

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Toomey, Dan, Lindsay Richman, Melissa Ferrari, Joseph Oladokun, and Wesley Banfield. JupyterLab Quick Start Guide: A Beginner's Guide to the Next-Gen, Web-based Interactive Computing Environment for Data Science. Packt Publishing, Limited, 2019.

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Chapitres de livres sur le sujet "JupyterLab"

Bisong, Ekaba. "JupyterLab Notebooks." In Building Machine Learning and Deep Learning Models on Google Cloud Platform. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4470-8_6.

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Brocker, Annabell, Sven Judel, Rene Roepke, Nikol Mihailovska, and Ulrik Schroeder. "Gamifying JupyterLab to Encourage Continuous Interaction in Programming Education." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-22124-8_32.

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Liang, Yibao, and Minoru Uehara. "How to Run GUI Applications on a Java Development Environment Based on JupyterLab." In Advanced Information Networking and Applications. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57840-3_26.

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Poojashree, K., S. Meena, and C. Celsiya. "A Comparative Study and Analysis on ML Algorithm for Defective Package Detection System With Quantum Networking Integration." In Advances in Computational Intelligence and Robotics. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-5832-0.ch006.

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The comparative study on ML algorithms for defective package detection is a comparison between different algorithms to classify the package as damaged or intact. This project addresses the critical concern of managing product quality in the field of manufacturing production. The primary objective of this project is to deliver a non-defective package to the customer. To achieve this goal, machine learning utilizes classification algorithms like the decision tree algorithm, the random forest algorithm, and the logistic regression algorithm. Data is collected from Kaggle, and that data is used to

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Resendez, Skyler, Gillian Franklin, Rachel Stephens, Heather Maness, Srikar Chamala, and Peter L. Elkin. "Analyzing the Efficacy of an Open Access Biomedical Informatics Boot Camp." In Studies in Health Technology and Informatics. IOS Press, 2024. http://dx.doi.org/10.3233/shti240711.

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Each summer, the University at Buffalo hosts a free, virtual biomedical informatics (BMI) boot camp. Lectures covering various subject matter areas are offered including, but not limited to: machine learning, natural language processing, programming, database queries, clinical decision support (CDS), and public and consumer health informatics. Once the 2023 boot camp had concluded, an anonymous, voluntary survey was offered to everyone who attended. The results of the boot camp were overwhelmingly positive with 70% of the survey participants indicating that they agreed that their expectations

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Actes de conférences sur le sujet "JupyterLab"

Ragan-Kelley, Min, Matt Henderson, Fernando Pérez, Rollin Thomas, Shreyas Cholia, and Lavanya Ramakrishnan. "Enabling Scientific Collaboration with JupyterHub." In SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE, 2024. https://doi.org/10.1109/scw63240.2024.00249.

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Brocker, Annabell, Sven Judel, Ulrik Schroeder, and Yanik Soltzer. "Juxl: JupyterLab xAPI Logging Interface." In 2022 International Conference on Advanced Learning Technologies (ICALT). IEEE, 2022. http://dx.doi.org/10.1109/icalt55010.2022.00054.

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Olney, Andrew M., Scott D. Fleming, and Jillian C. Johnson. "Learning Data Science with Blockly in JupyterLab." In SIGCSE '21: The 52nd ACM Technical Symposium on Computer Science Education. ACM, 2021. http://dx.doi.org/10.1145/3408877.3439534.

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Ochkov, Valery F., Alan Stevens, and Anton I. Tikhonov. "Jupyter Notebook, JupyterLab – Integrated Environment for STEM Education." In 2022 VI International Conference on Information Technologies in Engineering Education (Inforino). IEEE, 2022. http://dx.doi.org/10.1109/inforino53888.2022.9782924.

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Wells, Michael. "Improving your Python Notebook Workspace with Custom JupyterLab Extensions." In Proposed for presentation at the 2022 Tri-lab Advanced Simulation & Computing Sustainable Scientific Software Conference held May 24-26, 2022 in Albuquerque, NM. US DOE, 2022. http://dx.doi.org/10.2172/2003073.

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Kumar, Mandeep. "HPC/AI Deep Dive Training Experiences with Containers and JupyterLab." In 2022 International Conference on Knowledge Engineering and Communication Systems (ICKECS). IEEE, 2022. http://dx.doi.org/10.1109/ickecs56523.2022.10060302.

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Kokkinakis, Dimitrios. "From Zipf distribution to Universal Dependencies – Interactive Notebooks for Swedish Text Analysis." In Huminfra Conference (HiC 2024). Linköping University Electronic Press, 2024. http://dx.doi.org/10.3384/ecp205006.

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Notebook-based environments are powerful (web-based) interactive development resources for conducting exploratory (textual) data analysis (EDA). These environments allow the embedding of code (code snippets in ‛code cells’) which can be easily executed with the results immediately presented into the user’s window. This paper introduces some basic exploratory tools and techniques using JupyterLab notebooks, applied to Swedish using a subcorpus that address various topics related to the COVID-19 pandemic published during January-December 2021.

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Kumar, Mandeep, and Gagandeep Kaur. "Study of container-based JupyterLab and AI Framework on HPC with GPU usage." In 2022 International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON). IEEE, 2022. http://dx.doi.org/10.1109/smartgencon56628.2022.10084107.

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Harrison, Galen, Kevin Bryson, Ahmad Emmanuel Balla Bamba, et al. "JupyterLab in Retrograde: Contextual Notifications That Highlight Fairness and Bias Issues for Data Scientists." In CHI '24: CHI Conference on Human Factors in Computing Systems. ACM, 2024. http://dx.doi.org/10.1145/3613904.3642755.

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Vostokin, S., S. Popov, and O. Sukhoroslov. "EXPERIENCE IN ORGANIZING FLEXIBLE ACCESS TO REMOTE COMPUTING RESOURCES FROM JUPYTERLAB ENVIRONMENT USING TECHNOLOGIES OF EVEREST AND TEMPLET PROJECTS." In 9th International Conference "Distributed Computing and Grid Technologies in Science and Education". Crossref, 2021. http://dx.doi.org/10.54546/mlit.2021.94.77.001.

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The paper describes the experience of building distributed web applications based on the interactivecomputing technologies of the Jupyter project. The new architecture of such applications is proposed,considering the possibility of deploying a Jupyter notebook server separately from computingresources, and the possibility to interact with several computing resources simultaneously. Thesefeatures are implemented using the Everest platform for resource integration and the Templet SDK foraccessing the platform from Jupyter notebooks. Two examples of computing and data processingapplications built

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