• Suche: Introduction to computation and programming using Python

Treffer 81 - 100 von 214

82

Introduction to CS and Programming using Python
Massachusetts Institute of Technology ; Ana Bell

Engineering Computer Science Programming Languages Studienbereich Informati... Ingenieurwissenschaften
Zu den Favoriten
83

Signalų apdorojimo algoritmai su automatizuotu apmokymu
Lapinskas, Karolis ; Meškauskas, Tadas

Dissertation
Zu den Favoriten
84

2006 Survey Results for CS1310: Introduction to Programming Using Media Computation
Gandara, Aida
Departmental Technical Reports (CS)

Computer Engineering
Fachzeitschrift
Zu den Favoriten
85

Python in Astronomy
Sławomir Piasecki ; Kompetenzzentrum für nicht-textuelle Materialien

Computer Science Studienbereich Informati... Ingenieurwissenschaften
Zu den Favoriten
86

An Introduction to Sage
Arvind S Raj ; Kompetenzzentrum für nicht-textuelle Materialien

Computer Science Studienbereich Informati... Ingenieurwissenschaften
Zu den Favoriten
87

cgohlke/ipcf.ipynb: v2023.12.8
Gohlke, Christoph ; orcid:0000-0001-8108-

E-Ressource
Zu den Favoriten
89

Comparison of Multiprocessor and Multi-Threaded Implementations of the Entropy Approach to Impute Gaps in Data in Python ; Порівняння багатопроцесорної та багатопоточної реалізацій ентропійного підходу для імпутування пропусків у даних на мові програмування Python
Zemlianyi, Oleksii ; Baibuz, Oleh ; Земляний, Олексій ; et al.
Challenges and Issues of Modern Science; Vol. 2 (2024): Challenges and Issues of Modern Science; 300-304 ; 3083-5704 ; 10.15421/cims.2

data imputation multiprocessing multithreading computation optimization багатопроцесорність... багатопоточність... …
Fachzeitschrift
Zu den Favoriten
90

Applied Computational Thinking with Python: Design Algorithmic Solutions for Complex and Challenging Real-World Problems.
Jesús Sofía De. ; Martinez Dayrene.
EN05CEBSCO05C663

Computer algorithms Python (Computer program... Algorithms Algorithmes Python (Langage de progr... Object-oriented programm... …
Buch
Zu den Favoriten
91

Utilizarea Inteligenţei Artificiale în tehnologia blockchain ; Using Artificial Intelligence in blockchain technology
CHELEȘ, Ivan ; CĂRBUNE, Viorel

inteligență artificială... blockchain сriptare securitate digitală analiză de date lanțuri de aprovizionare... …
Dissertation
Zu den Favoriten
92

Development of random forest machine learning model for the detection of changes in liver tissue after exposure to iron oxide nanoparticles
Paunović-Pantić, Jovana ; Vučević, Danijela ; Pantić, Igor ; et al.
Medicinska istraživanja (2024) 57(1):21-26 ; ISSN: 0301-0619

artificial Intelligence machine learning iron oxide liver
Fachzeitschrift
Zu den Favoriten
93

An introduction to PyTorch & Autograd
Paul O'Grady ; Kompetenzzentrum für nicht-textuelle Materialien

Computer Science Studienbereich Informati... Ingenieurwissenschaften
Zu den Favoriten
94

Do models for paired-word recognition capture manipulations in the way they are meant to do? A model validation study.
Voormann, Anne ; Spektor, Mikhail S. ; Klauer, Karl Christoph
Journal of Experimental Psychology: Human Learning and Memory

Fachzeitschrift
Zu den Favoriten
96

Implementación de redes neuronales utilizando un esquema evolutivo orientado a problemas de clasificación
Vargas Ochoa, Carlos Edwardo ; Ugarte Macías, Juan Pablo
[1] G. P. Zhang, «Neural Networks for Classification: A Survey,» IEEE Transactions on Systems, Man and Cybernetics, vol. 30, nº 4, p. 451–462, 2000. ; [2] B. Widrow, D. E. Rumelhart y M. A. Lehr, «Neural networks: applications in industry, business and science,» Communications of the ACM, vol. 37, nº 3, p. 93–105, 1994. ; [3] H. Lu, R. Setiono y H. Liu., «Effective data mining using neural networks,» IEEE Transactions on Knowledge and Data Engineering, vol. 8, nº 6, p. 957–961, 1996. ; [4] R. Bala y D. Kumar, «Classification Using ANN: A Review,» International Journal of Computational ....

620 - Ingeniería y opera... Maquina Aprendizaje Datos Redes Neuronales Redes Neuronales Evoluti... …
Dissertation
Zu den Favoriten
97

Automatic implicit motive codings are at least as accurate as humans’ and 99% faster.
Nilsson, August Håkan ; Runge, J. Malte ; Ganesan, Adithya V. ; et al.
J Pers Soc Psychol

Fachzeitschrift
Zu den Favoriten
100

Unsupervised machine learning for the classification of astrophysical X-ray sources ; Aprendizaje Automático No Supervisado para la Clasificación de Fuentes Astrofísicas de Rayos X
Pérez Díaz, Víctor Samuel
Merloni, A; Predehl, P; Becker, W; Böhringer, H; Boller, T; Brunner, H; Brusa, M; Dennerl, K; Freyberg, M; Friedrich, P; Georgakakis, A; Haberl, F; Hasinger, G; Meidinger, N; Mohr, J; Nandra, K; Rau, A; Reiprich, T H; Robrade, J; Salvato, M; Santangelo, A; Sasaki, M; Schwope, A; Wilms, J; Consortium, The German Erosita (2012) eROSITA Science Book: Mapping the Structure of the Energetic Universe. En: arXiv [astro-ph.HE]. Disponible en: http://arxiv.org/abs/1209.3114. ; Van Rossum, Guido; Drake, Fred L (2009) Python 3 Reference Manual. Scotts Valley, CA: CreateSpace; 9781441412690; ; Harris, Charles R; Millman, K Jarrod; van der Walt, Stéfan J; ....

Clustering GMM K-means Chandra Source Catalog Chandra X-ray Observator... Clasificación …
Dissertation
Zu den Favoriten
Suchwerkzeuge: RSS-Feed abonnieren Diese Suche als E-Mail versenden Suche speichern

Filter