Academic literature on the topic 'Langage Python'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Langage Python.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Langage Python":
Laurent Bloch. "Un langage pour enseigner la programmation, Scheme ou Python ?" Bulletin 1024, no. 20 (November 2022): 85–95. http://dx.doi.org/10.48556/sif.1024.20.85.
Philippot, Alexandre, Stéphane Lecasse, Bernard Riera, and François Gellot. "Développement d’un connecteur logiciel pour l’apprentissage de l’automatisme." J3eA 21 (2022): 2056. http://dx.doi.org/10.1051/j3ea/20222056.
KENOUFI, Abdelouahab. "Probabilist Set Inversion using Pseudo-Intervals Arithmetic." TEMA (São Carlos) 15, no. 1 (March 5, 2014): 097. http://dx.doi.org/10.5540/tema.2014.015.01.0097.
Jovanović, S., and S. Weber. "Modélisation et accélération de réseaux de neurones profonds (CNN) en Python/VHDL/C++ et leur vérification et test à l’aide de l’environnement Pynq sur les FPGA Xilinx." J3eA 21 (2022): 1028. http://dx.doi.org/10.1051/j3ea/20220028.
Graillet, Olivia, Frédéric Alicalapa, Pierre-Olivier Lucas de Peslouan, Denis Genon-Catalot, and Jean-Pierre Chabriat. "Approche pédagogique pour l’étude d’autoconsommation photovoltaïque au niveau Master avec utilisation de l’API de SolarIO en langage Python." J3eA 23 (2024): 0002. http://dx.doi.org/10.1051/j3ea/20240002.
Akeel Hussein Alaasam, Hussein, Ahmed Ali Talib Al-Khazaali, Ali Hussein Aleiwi, and Doaa Wahhab Ibrahim. "Learn Land Features Using Python Language." BIO Web of Conferences 97 (2024): 00111. http://dx.doi.org/10.1051/bioconf/20249700111.
Gujar, Advait. "C vs Python: A Cursory Look with Industry Opinion." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (October 31, 2023): 55–64. http://dx.doi.org/10.22214/ijraset.2023.56446.
Peta, Saphalya. "Python- An Appetite for the Software Industry." International Journal of Programming Languages and Applications 12, no. 4 (October 31, 2022): 1–14. http://dx.doi.org/10.5121/ijpla.2022.12401.
Patel, Aryan. "Mojo: A Python-based Language for High-Performance AI Models and Deployment." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (October 1, 2023): 1–11. http://dx.doi.org/10.55041/ijsrem26529.
Lazebna, Nataliia. "ENGLISH-LANGUAGE BASIS OF PYTHON PROGRAMMING LANGUAGE." Research Bulletin Series Philological Sciences 1, no. 193 (April 2021): 371–76. http://dx.doi.org/10.36550/2522-4077-2021-1-193-371-376.
Dissertations / Theses on the topic "Langage Python":
Miled, Mahdi. "Ressources et parcours pour l'apprentissage du langage Python : aide à la navigation individualisée dans un hypermédia épistémique à partir de traces." Thesis, Cachan, Ecole normale supérieure, 2014. http://www.theses.fr/2014DENS0045/document.
This research work mainly concerns means of assistance in individualized navigation through an epistemic hypermedia. We have a number of resources that can be formalized by a directed acyclic graph (DAG) called the graph of epistemes. After identifying resources and pathways environments, methods of visualization and navigation, tracking, adaptation and data mining, we presented an approach correlating activities of design or editing with those dedicated to resources‘ use and navigation. This provides ways of navigation‘s individualization in an environment which aims to be evolutive. Then, we built prototypes to test the graph of epistemes. One of these prototypes was integrated into an existing platform. This epistemic hypermedia called HiPPY provides resources and pathways on Python language. It is based on a graph of epistemes, a dynamic navigation and a personalized knowledge diagnosis. This prototype, which was experimented, gave us the opportunity to evaluate the introduced principles and analyze certain uses
Tesser, Federico. "Solveur parallèle pour l’équation de Poisson sur mailles superposées et hiérarchiques, dans le cadre du langage Python." Thesis, Bordeaux, 2018. http://www.theses.fr/2018BORD0129/document.
Adaptive discretizations are important in compressible/incompressible flow problems since it is often necessary to resolve details on multiple levels,allowing large regions of space to be modeled using a reduced number of degrees of freedom (reducing the computational time).There are a wide variety of methods for adaptively discretizing space, but Cartesian grids have often outperformed them even at high resolutions due totheir simple and accurate numerical stencils and their superior parallel performances.Such performance and simplicity are in general obtained applying afinite-difference scheme for the resolution of the problems involved, but this discretization approach does not present, by contrast, an easy adapting path.In a finite-volume scheme, instead, we can incorporate different types of grids,more suitable for adaptive refinements, increasing the complexity on thestencils and getting a greater flexibility.The Laplace operator is an essential building block of the Navier-Stokes equations, a model that governs fluid flows, but it occurs also in differential equations that describe many other physical phenomena, such as electric and gravitational potentials, and quantum mechanics. So, it is a very importantdifferential operator, and all the studies carried out on it, prove itsrelevance.In this work will be presented 2D finite-difference and finite-volume approaches to solve the Laplacian operator, applying patches of overlapping grids where amore fined level is needed, leaving coarser meshes in the rest of the computational domain.These overlapping grids will have generic quadrilateral shapes.Specifically, the topics covered will be:1) introduction to the finite difference method, finite volume method, domainpartitioning, solution approximation;2) overview of different types of meshes to represent in a discrete way thegeometry involved in a problem, with a focuson the octree data structure, presenting PABLO and PABLitO. The first one is anexternal library used to manage each single grid’s creation, load balancing and internal communications, while the second one is the Python API ofthat library written ad hoc for the current project;3) presentation of the algorithm used to communicate data between meshes (beingall of them unaware of each other’s existence) using MPI inter-communicators and clarification of the monolithic approach applied building the finalmatrix for the system to solve, taking into account diagonal, restriction and prolongation blocks;4) presentation of some results; conclusions, references.It is important to underline that everything is done under Python as programmingframework, using Cython for the writing of PABLitO, MPI4Py for the communications between grids, PETSc4py for the assembling and resolution partsof the system of unknowns, NumPy for contiguous memory buffer objects.The choice of this programming language has been made because Python, easy to learn and understand, is today a significant contender for the numerical computing and HPC ecosystem, thanks to its clean style, its packages, its compilers and, why not, its specific architecture optimized versions
Monat, Raphaël. "Static type and value analysis by abstract interpretation of Python programs with native C libraries." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS263.
In this thesis, we aim at designing both theoretically and experimentally methods for the automatic detection of potential bugs in software – or the proof of the absence thereof. This detection is done statically by analyzing programs’ source code without running them. We rely on the abstract interpretation framework to derive sound, computable semantics. In particular, we focus on analyzing dynamic programming languages. The target of this work is the analysis of Python programs combined with native C libraries
Huth, Jacob. "Modelling Aging in the Visual System & The Convis Python Toolbox." Electronic Thesis or Diss., Sorbonne université, 2018. http://www.theses.fr/2018SORUS140.
In this thesis we investigate aging processes in the visual system from a computational modelling perspective. We give a review about neural aging phenomena, basic aging changes and possible mechanisms that can connect causes and effects. The hypotheses we formulate from this review are: the input noise hypothesis, the plasticity hypothesis, the white matter hypothesis and the inhibition hypothesis. Since the input noise hypothesis has the possibility to explain a number of aging phenomena from a very simple premise, we focus mainly on this theory. Since the size and organization of receptive fields is important for perception and is changing in high age, we developed a theory about the interaction of noise and receptive field structure. We then propose spike-time dependent plasticity (STDP) as a possible mechanism that could change receptive field size in response to input noise. In two separate chapters we investigate the approaches to model neural data and psychophysical data respectively. In this process we examine a contrast gain control mechanism and a simplified cortical model respectively. Finally, we present convis, a Python toolbox for creating convolutional vision models,which was developed during the studies for this thesis. convis can implement the most important models used currently to model responses of retinal ganglion cells and cells in the lower visual cortices (V1 and V2)
Bleuzé, Alexandre. "Transfert d'apprentissage intra et inter sujets en interfaces cerveau-machine non-invasives." Electronic Thesis or Diss., Université Grenoble Alpes, 2023. http://www.theses.fr/2023GRALS057.
A brain-computer interface (BCI) is a direct link between a brain and a computer, enabling an individual to perform tasks without the need for peripheral nerves or muscles. In recent years, BCIs have become increasingly interesting, especially in the healthcare sector, because of their potential to help patients. They have been used to help some people recover their motor functions after a stroke or spinal cord injury, or to help people with degenerative diseases such as amyotrophic lateral sclerosis, who gradually lose the ability to control their limbs and then communicate. Another factor adding to the appeal of BCIs is their potential to enhance the capabilities of healthy people in areas such as video games. Today, thanks to technological advances in the healthcare field, the tools needed to set up BCIs, such as electroencephalograms, are becoming more affordable, enabling the multiplication of experiments and clinical tests, giving access to a vast amount of data, sometimes freely available on the Internet. This data could make it possible to create models that have been trained from the data of many people, thereby increasing the performance of future systems while reducing their calibration time. This would also enable the use of less expensive hardware for equivalent performance, making BCIs more affordable. The main problem today is that the data available in open access is very heterogeneous, whether in terms of quality, paradigm or even simply hardware. For these reasons, it is very difficult to exploit all this data to extract common features. The aim of this thesis is to find methods for adapting and using open-access data to create machine learning models that are highly robust because they are trained on data from a wide range of subjects. To this end, we are focusing on Riemannian geometry, the use of which in brain-computer interfaces has recently shown to be highly effective. More specifically, this original work focuses on the development of transfer learning methods in the tangent space of the Riemannian variety. The proposed methods have been evaluated on a large number of databases covering several paradigms: motor imagery, P300 evoked potentials and steady-state visual evoked potentials. The work carried out in this thesis has led to the development of a method called Tangent Space Alignment (TSA), which achieves an overall improvement in accuracy of 2.7% over a previously published Riemannian method, Riemannian Procrustes Analysis (RPA). Another contribution of this thesis to the scientific community is research into the use of mathematical arbitrary sources in BCI transfer learning. The work carried out in this thesis shows that little information is lost when aligning to this arbitrary source, and studies the impact on accuracy between subjects, enabling new alignment possibilities to be explored and mathematically normalized alignment sources to be sought, rather than existing subject data which may not possess the right mathematical properties to serve as a quality source
Larouche, Tremblay François. "Analyse détaillée du fonctionnement interne du schéma de surface CLASS." Master's thesis, Université Laval, 2014. http://hdl.handle.net/20.500.11794/25359.
Canadian Land Surface Scheme
Silva, Bruno Hartmann da. "Nano-κ : a Python code for the multiscale modelling of the thermal conductivity." Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0212.
Electronic devices are present in almost every aspect of modern society and their optimisation and control is of paramount importance in the development of new technologies. In addition, environmental concerns about their energy efficiency and lifetime require the testing of alternatives that minimise human impact on nature. One of the most common materials used in electronic nanodevices is semiconductors, such as silicon (Si) and germanium (Ge). In this context, there is a strong motivation to study phonons, quanta of crystal lattice vibration, which are the main carriers of thermal energy in semiconductors. At the macroscale, material properties such as thermal conductivity are usually considered to be independent of boundary conditions. This is not the case at the nanoscale, where each vibrational mode of the material can behave differently due to the geometric configuration. This requires a more detailed calculation to understand how geometric parameters affect the ability of the nanodevice to conduct heat. Understanding heat conduction at the nanoscale is important to avoid overheating the system and to understand how temperature affects its electrical performance. Computational tools could efficiently provide great insights to understand these effects. In fact, several works have already used numerical calculations to understand the thermal behaviour of nanodevices, but usually with in-house codes that are not open to the community. In this context, this thesis presents Nano-κ, a Python code to solve the Boltzmann transport equation (BTE) in nanodevices using the Monte Carlo method with ab initio data as input. First, the theory behind phonon transport and its computational implementation in Nano-κ is discussed. Then, a sensitivity analysis is performed to verify the effect of the main simulation parameters on the estimated thermal conductivity. The thermal conductivity calculated by Nano-κ is then compared with results from the literature in several thin film and nanowire settings, which in general show good agreement. In addition, an arbitrary geometry is simulated in two different cases, demonstrating Nano-κ's flexibility and consistency in providing good estimates of heat transfer in nanodevices. The thesis concludes by suggesting possible avenues for improvement in future work
Hold-Geoffroy, Yannick. "SCOOP : cadriciel de calcul distribué générique." Master's thesis, Université Laval, 2015. http://hdl.handle.net/20.500.11794/25711.
This paper presents SCOOP, a new Python framework for automatically distributing dynamic task hierarchies focused on simplicity. A task hierarchy refers to tasks that can recursively spawn an arbitrary number of subtasks. The underlying computing infrastructure consists of a simple list of resources. The typical use case is to run the user’s main program under the umbrella of the SCOOP module, where it becomes a root task that can spawn any number of subtasks through the standard “futures” API of Python, and where these subtasks may themselves spawn other subsubtasks, etc. The full task hierarchy is dynamic in the sense that it is unknown until the end of the last running task. SCOOP automatically distributes tasks amongst available resources using dynamic load balancing. A task is nothing more than a Python callable object in conjunction with its arguments. The user need not worry about message passing implementation details; all communications are implicit.
Combrisson, Etienne. "Décodage des intentions et des exécutions motrices : étude du rôle des oscillations cérébrales via l’apprentissage machine et développement d’outils open-source." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSE1327/document.
The execution of a motor task is associated with complex patterns of oscillatory modulations in the brain. However, the specific role of oscillatory phase, amplitude and phase-amplitude coupling (PAC) across the planning and execution stages of goal-directed motor behavior is still not yet fully understood. The aim of the first part of this PhD thesis was to address this question by analyzing intracranial EEG data recorded in epilepsy patients during the performance of a delayed center-out task. Using machine learning, we identified functionally relevant oscillatory features via their accuracy in predicting motor states and movement directions. In addition to the established role of oscillatory power, our data-driven approach revealed the prominent role of low-frequency phase as well as significant involvement of PAC in the neuronal underpinnings of motor planning and execution. In parallel to this empirical research, an important portion of this PhD work was dedicated to the development of efficient tools to analyze and visualize electrophysiological brain data. These packages include a feature extraction and classification toolbox (Brainpipe), modular and tensor-based PAC computation tools (Tensorpac) and a versatile brain data visualization GUI (Visbrain). Taken together, this body of research advances our understanding of the role of brain oscillations in goal-directed behavior, and provides efficient open-source packages for the scientific community to replicate and extend this research
Häggholm, Petter. "PyRemote : object mobility in the Python programming language." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/31573.
Science, Faculty of
Computer Science, Department of
Graduate
Books on the topic "Langage Python":
Martelli, Alex. Python en concentré. 2nd ed. Paris: O'Reilly, 2007.
Budd, Timothy. Exploring Python. Dubuque, IA: McGraw-Hill, 2009.
Lutz, Mark. Python: Pocket Reference. Edited by Jonathan Gennick. 3rd ed. Beijing: O’Reilly Media, 2005.
Lutz, Mark. Learning Python. 3rd ed. Sebastopol, CA: O'Reilly, 2008.
Lutz, Mark. Learning Python. Beijing: O'Reilly, 1999.
Lutz, Mark. Learning Python. 2nd ed. Beijing: O'Reilly, 2003.
Lutz, Mark. Learning Python. 2nd ed. Sebastopol, CA: O'Reilly, 2004.
Lutz, Mark. Learning Python. 4th ed. CA 95472: O'Reilly, 2009.
Lutz, Mark. Einführung in Python. Beijing: O'Reilly, 2000.
Ziadé, Tarek. Programmation Python: Conception et optimisation. 2nd ed. Paris: Eyrolles, 2009.
Book chapters on the topic "Langage Python":
Browning, J. Burton, and Marty Alchin. "Python Language Moratorium." In Pro Python, 347–50. Berkeley, CA: Apress, 2014. http://dx.doi.org/10.1007/978-1-4842-0334-7_18.
Cannon, Brett, Jesse Noller, and Guido van Rossum. "Python Language Moratorium." In Pro Python, 317–19. Berkeley, CA: Apress, 2010. http://dx.doi.org/10.1007/978-1-4302-2758-8_18.
Gupta, Pramod, and Anupam Bagchi. "Python Language Basics." In Essentials of Python for Artificial Intelligence and Machine Learning, 87–126. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-43725-0_3.
Nakao, Masahiro. "Mixed-Language Programming with XcalableMP." In XcalableMP PGAS Programming Language, 147–63. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7683-6_5.
Köhl, Maximilian A., Michaela Klauck, and Holger Hermanns. "Momba: JANI Meets Python." In Tools and Algorithms for the Construction and Analysis of Systems, 389–98. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72013-1_23.
Dhaliwal, C. K., Poonam Rana, and T. P. S. Brar. "Introduction to Python Language." In Python Programming, 1–23. London: CRC Press, 2024. http://dx.doi.org/10.1201/9781032691053-1.
Mehare, Hussam Bin, Jishnu Pillai Anilkumar, and Naushad Ahmad Usmani. "The Python Programming Language." In A Guide to Applied Machine Learning for Biologists, 27–60. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-22206-1_2.
Kumar, Sunil. "Introduction to Python Language." In Python for Accounting and Finance, 11–30. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54680-8_2.
Schäfer, Christoph. "Overview of the Programming Language Python." In Quickstart Python, 1–2. Wiesbaden: Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-33552-6_1.
Danial, Albert. "Language Basics." In Python for MATLAB Development, 23–63. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-7223-7_3.
Conference papers on the topic "Langage Python":
Carter, H., Jason Rupert, Alexander Chan, and Chris Vinegar. "Concerns with using Python in Machine Learning Flight Critical Applications." In Vertical Flight Society 79th Annual Forum & Technology Display. The Vertical Flight Society, 2023. http://dx.doi.org/10.4050/f-0079-2023-18015.
Reiss, Frederick, Bryan Cutler, and Zachary Eichenberger. "Natural Language Processing with Pandas DataFrames." In Python in Science Conference. SciPy, 2021. http://dx.doi.org/10.25080/majora-1b6fd038-006.
Singh, Jyotika. "Social Media Analysis using Natural Language Processing Techniques." In Python in Science Conference. SciPy, 2021. http://dx.doi.org/10.25080/majora-1b6fd038-009.
Chapman, Brian, and Jeannie Irwin. "Python as a First Programming Language for Biomedical Scientists." In Python in Science Conference. SciPy, 2015. http://dx.doi.org/10.25080/majora-7b98e3ed-002.
Красноусов, Виктор Михайлович, Леонид Вячеславович Букреев, Георигий Андреевивич Шпаковский, Евгений Романович Калюжный, and Наталья Вячеславовна Зариковская. "MOBILE APPLICATIONS FOR THE ANDROID OPERATING SYSTEM: DEVELOPMENT TECHNOLOGIES." In Сборник избранных статей по материалам научных конференций ГНИИ "Нацразвитие" (Санкт-Петербург, Август 2021). Crossref, 2021. http://dx.doi.org/10.37539/aug298.2021.28.84.027.
Jin, Eric, and Yu Sun. "An Algorithm-Adaptive Source Code Converter to Automate the Translation from Python to Java." In 5th International Conference on Computer Science and Information Technology (COMIT 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.111719.
Qi, Peng, Yuhao Zhang, Yuhui Zhang, Jason Bolton, and Christopher D. Manning. "Stanza: A Python Natural Language Processing Toolkit for Many Human Languages." In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations. Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.acl-demos.14.
Singh, Gurpartap, Anup Lal Yadav, and Satbir S Sehgal. "Sign language recognition Using Python." In 2022 International Conference on Cyber Resilience (ICCR). IEEE, 2022. http://dx.doi.org/10.1109/iccr56254.2022.9996001.
Dobesova, Zdena. "Programming language Python for data processing." In 2011 International Conference on Electrical and Control Engineering (ICECE). IEEE, 2011. http://dx.doi.org/10.1109/iceceng.2011.6057428.
Drozd, Aleksandr, Anna Gladkova, and Satoshi Matsuoka. "Python, performance, and natural language processing." In the 5th Workshop. New York, New York, USA: ACM Press, 2015. http://dx.doi.org/10.1145/2835857.2835858.
Reports on the topic "Langage Python":
Zhu, Minjie, and Michael Scott. Fluid-Structure Interaction and Python-Scripting Capabilities in OpenSees. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, August 2019. http://dx.doi.org/10.55461/vdix3057.
Prasad, Jayanti. Large Language Models: AI Foundations and Applications in Python. Instats Inc., 2023. http://dx.doi.org/10.61700/85rfezw01y0q9521.
Kohler, Karsten. Teaching macroeconomics with an open-source online model simulation in R and Python. The Economics Network, February 2024. http://dx.doi.org/10.53593/n3917a.
Saltus, Christina, Todd Swannack, and S. McKay. Geospatial Suitability Indices Toolbox (GSI Toolbox). Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/41881.
Liu, X., Z. Chen, and S. E. Grasby. Using shallow temperature measurements to evaluate thermal flux anomalies in the southern Mount Meager volcanic area, British Columbia, Canada. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/330009.
Saltus, Christina, S. McKay, and Todd Swannack. Geospatial suitability indices (GSI) toolbox : user's guide. Engineer Research and Development Center (U.S.), August 2022. http://dx.doi.org/10.21079/11681/45128.
Тарасова, Олена Юріївна, and Ірина Сергіївна Мінтій. Web application for facial wrinkle recognition. Кривий Ріг, КДПУ, 2022. http://dx.doi.org/10.31812/123456789/7012.
Sladen, W. E., R. J. H. Parker, P. D. Morse, S V Kokelj, and S. L. Smith. Geomorphic feature inventory along the Dempster and Inuvik to Tuktoyaktuk highway corridor, Yukon and Northwest Territories. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/329969.
Mbani, Benson, Timm Schoening, and Jens Greinert. Automated and Integrated Seafloor Classification Workflow (AI-SCW). GEOMAR, May 2023. http://dx.doi.org/10.3289/sw_2_2023.
Le Béchec, Mariannig, Aline Bouchard, Philippe Charrier, Claire Denecker, Gabriel Gallezot, and Stéphanie Rennes. State of open science practices in france (SOSP-FR). Ministère de l'enseignement supérieur et de la recherche, January 2022. http://dx.doi.org/10.52949/5.