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AG Mathematics of Deep Learning /CoursesID:878
- Most recent entry on 2021-07-26
Keywords:
functional
minimization
methods
framework
approximation
control
distance
reconstruction
energy
deep
search
basic
weights
models
measure
layer
activation
problem
example
propagation
Lecturer
Dr. Daniel Tenbrinck
Organisational Unit
Lehrstuhl für Angewandte Mathematik
Recording type
Vortragsreihe
Via
Free / IdM-login
Language
Associated Clips
Episode
Title
Lecturer
Updated
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1
Introduction to Deep Learning
Dr.-Ing. Vincent Christlein
2019-11-14
Free
01:22:04
slides
audio
captions
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2
Mathematics - Deep Learning
Prof. Dr. Martin Burger
2019-12-05
Free
01:35:25
slides
slides
audio
captions
go to clip page
3
Known Operator Learning - Towards Integration of Prior Knowledge into Machine Learning
Prof. Dr. Andreas Maier
2020-01-09
Free
00:44:56
slides
slides
audio
captions
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4
Biomechanics meets Optimal Control: Predictions of Human Motion
Prof. Dr. Anne Koelewijn
2020-02-13
Free
01:27:21
slides
slides
audio
captions
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5
A New Approach for Stochastic Optimization in Deep Learning
Dr. Lukas Pflug
2020-02-20
Free
00:31:51
slides
slides
audio
captions
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6
Introduction to Adversarial Robustness for Deep Learning
Leo Schwinn
2020-11-11
Free
00:53:41
slides
captions
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7
The interplay of deep learning and control theory
2020-12-09
Free
01:04:25
slides
captions
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8
Generalised Perceptron Learning
2020-12-16
Free
01:10:53
slides
captions
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9
Neuromorphic Computing and its Mathematical Challenges
2021-01-13
Free
01:17:23
slides
captions
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10
Structure Preserving Deep Learning
2021-01-27
Free
01:07:37
slides
captions
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11
Mathematical aspects of neural network approximation and learning
2021-04-20
Free
01:02:09
slides
captions
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12
Deep Learning meets Shearlets: On the Path Towards Interpretable Imaging
2021-04-27
Free
00:50:44
slides
captions
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13
On the Confluence of Deep Learning and Energy Minimization Methods for Inverse Problems
2021-05-04
IdM-login
00:53:30
slides
captions
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14
Neural Differential Equations, Control and Machine Learning
2021-05-11
Free
00:57:53
slides
captions
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15
An Introduction to Generative Modeling
2021-05-18
Free
00:55:59
slides
captions
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16
Random walks and PDEs in graph-based learning
2021-05-25
Free
01:00:32
slides
captions
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17
Learning with energy-based models
2021-06-01
Free
01:04:59
slides
captions
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18
Learning of Wasserstein Generative Models and Patch-based Texture Synthesis
2021-06-08
Free
01:01:40
slides
captions
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19
Self-supervised Learning for 3D Shape Analysis
2021-06-18
Free
00:34:20
slides
captions
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20
Deep Learning for Computed Tomography Image Reconstruction from Insufficient Data
Dr. Yixing Huang
2021-06-26
Free
00:41:12
slides
captions
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21
Deeply learned regularisation for inverse problems
2021-06-29
Free
00:58:36
slides
captions
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22
Online Learning for Optimization Problems with Unknown or Uncertain Cost Functions
2021-07-06
Free
00:29:47
slides
captions
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23
Biomechanics meets Deep Learning
Prof. Dr. Anne Koelewijn
2021-07-13
Free
01:10:13
slides
captions
go to clip page
24
Accelerated Forward-Backward Optimization using Deep Learning
2021-07-26
Free
00:46:52
slides
captions
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More courses from Dr. Daniel Tenbrinck
Mathematical Data Science 1 2021
Dr. Daniel Tenbrinck
lecture
2021-06-17
Free
Mathematik für Physikstudierende C 2021/2022
Dr. Daniel Tenbrinck
lecture
2022-02-17
Free
Mathematik für Data Science 2 / Physikstudierende B 2021
Dr. Daniel Tenbrinck
lecture
2021-07-16
Free
Orientierungswoche Data Science 2021/2022
Dr. Daniel Tenbrinck
lecture
2021-10-14
Passwort / Studon
Discrete Graph Operators for Manifold-Valued Data
Dr. Daniel Tenbrinck
2020-11-04
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