Filters
total: 9116
filtered: 278
-
Catalog
- Publications 5575 available results
- Journals 185 available results
- Conferences 79 available results
- People 175 available results
- Inventions 13 available results
- Projects 20 available results
- Laboratories 2 available results
- Research Teams 1 available results
- e-Learning Courses 278 available results
- Events 33 available results
- Open Research Data 2755 available results
Chosen catalog filters
Search results for: RGB-D DATA
-
Advanced Data Mining 2022/23
e-Learning Courses -
Numerical Methods - Data Engineering - 2023
e-Learning Coursesstudia inżynierskie, informatyka i inżynieria danych
-
Business Data Analytics-2024 /2025
e-Learning Courses -
Data analysis - team project 2024
e-Learning Courses -
Analiza danych typu Big Data
e-Learning Courses -
Numerical Methods - Data Engineering - 2024
e-Learning CoursesInżynieria danych
-
Business Data Analytics-2023 /2024
e-Learning Courses -
Big Data ST (2024/2025_zima)
e-Learning Courses -
Inżynieria Danych Data Science 2024
e-Learning Courses -
Advanced Data Mining 2023/24
e-Learning Courses -
INFRO@D Roadside safety management
e-Learning Courses -
INFRO@D Road pavement management
e-Learning Courses -
DATA MINING NSTAC 2022 ON-LINE
e-Learning CoursesNSTAC
-
Business data semantics and representation 2023
e-Learning Courses -
Visualization of economic data 2024/2025
e-Learning Courses -
Business data semantics and representation - 2024
e-Learning Courses -
Business data semantics and representation 2024
e-Learning Courses -
Visualization of Economic Data - 2023/24
e-Learning Courses -
Frameworks and tools for data engineers 2025_2026
e-Learning Courses -
Repetytorium: POP + WAI + HiH (Data Engineering)
e-Learning Courses -
Qualitative Data Analysis Methods -Summer 2022
e-Learning Courses -
Operating systems (Data Engineering) - 2023/24
e-Learning Courses -
Algorithms & Data Structures 2022/23
e-Learning Courses -
Algorithms & Data Structures 23/24
e-Learning Courses -
Big Data 2024 nst (2024/2025_zima)
e-Learning Courses -
Operating systems (Data Engineering) - 2024/25
e-Learning Courses -
WCH 5 sem B2 D. Zalewska6
e-Learning Courses -
WIMIO / OCE III sem D. Zalewska1
e-Learning Courses -
WCH III sem C1 D. Zalewska5
e-Learning Courses -
D. Zalewska 5 ACiR i IBM
e-Learning Courses -
[ITiT] Technologies of Spatial Data Analysis and Processing
e-Learning Courses{mlang pl} Dyscyplina: Informatyka Techniczna i Telekomunikacja Zajęcia obowiązkowe dla doktorantów II roku Prowadzący: dr hab. inż. Marcin Kulawiak Liczba godzin: 30 h Forma zajęć: wykład/seminarium {mlang} {mlang en} Discipline: Technical Informatics and Telecommunications Obligatory course for 2nd year PhD students Academic teacher: dr hab. inż. Marcin Kulawiak Total hours of training: 30 teaching hours Course...
-
Scientific methods of computer data analysis and presentation
e-Learning Courses -
Qualitative Data Analysis Methods -Summer 22/23
e-Learning Courses -
Data Warehouses - Part-time studies - 2022/2023
e-Learning CoursesThe curse is led for part-time studies, on the first semester of postgraduate studies.
-
WETI (Data Engineering) - Mathematics 2021/22 (M.Musielak)
e-Learning Courses -
WETI (Data Engineering) - Mathematics 2022/23 (M.Musielak)
e-Learning Courses -
WETI (Data Engineering) - Mathematics 2019/20 (M.Musielak)
e-Learning Courses -
Data Warehouses - Part-time studies - 2023/2024
e-Learning CoursesThe curse is led for part-time studies, on the first semester of postgraduate studies.
-
Analiza danych typu Big Data 2023/24
e-Learning Courses -
Chemia Bud III sem (B2) D. Zalewska3
e-Learning Courses -
Informatyka II sem C1 D. Zalewska 6
e-Learning Courses -
Analiza Gospodarcza III sem C1 D. Zalewska4
e-Learning Courses -
Budownictwo Niestacjonarne IIst B2 D. Zalewska 5
e-Learning Courses -
WETI - Informatyka - Matematyka 2019/2020 (D. Żarek)
e-Learning Courses -
WCH IV sem C1 D. Zalewska 2
e-Learning Courses -
WCH 6 sem B2 D. Zalewska 8
e-Learning Courses -
Deep neural networks for data analysis 24/25
e-Learning CoursesThis course covers introduction to supervised machine learning, construction of basic artificial deep neural networks (DNNs) and basic training algorithms, as well as the overview of popular DNNs architectures (convolutional networks, recurrent networks, transformers). The course introduces students to popular regularization techniques for deep models. Besides theory, large part of the course is the project in which students apply...
-
Deep neural networks for data analysis 27/28
e-Learning Courses -
Deep neural networks for data analysis 25/26
e-Learning Courses -
Deep neural networks for data analysis 26/27
e-Learning Courses