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fe1170e3-5c3b-4b39-a5d2-63992afab871
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http://gch.utwente.nl/data/concept/d2124ea2-6559-4d81-8703-882a2779c812
General information
Learning outcomes
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Topics
Topics
convolutional neural network
advanced deep learning methods
Deep Aggregation Transformer network
deep learning algorithms application
geospatial science
deep learning algorithms generalization
generative networks
advanced algorithms
deep learning research
presentation
GRU network
foundation models
convolutional operations
semi-supervised learning
object outlines in vector format
deep learning model
ViT
advanced architectures
neural network
ML
Data-efficient image Transformers
different architectures
loss functions optimization algorithms
deep learning model results interpretation
Transformers
deep learning algorithms performance
deep learning algorithms
Recurrent Neural Networks
SDGs
assignment
time series analysis
spatial data types
explainable AI
deep learning principles
MViT
Fully convolutional networks
use cases
advanced machine learning algorithms
LSTM
pooling
transfer learning
RNN
geospatial data
training deep networks
AI
geoinformatics
ethical considerations
test
activation functions
spatio-temporal data types
EO
deep learning model results
extracting object outlines
format learning activities
transformer networks
fully connected layers
deep learning algorithms capabilities
deep learning methods
SWIN
explainable AI techniques concepts
regularisation techniques
instance segmentation
explainable AI techniques
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UNIVERSITY OF TWENTE
Faculty of Geo-Information Science and Earth Observation
GeoCourseHub at Utwente.nl