Deep Learning
PIM-DL
P221-0155
pim2
2
V
2
P
6
3
nein
Englisch
Projektarbeit (Präsentation und Dokumentation)
E2831
Elektro- und Informationstechnik
3
Wahlpflichtfach
KIM-DL
Kommunikationsinformatik
3
Wahlpflichtfach
PIM-DL
Praktische Informatik
3
Wahlpflichtfach
Die Präsenzzeit dieses Moduls umfasst bei 15 Semesterwochen 60 Veranstaltungsstunden (= 45 Zeitstunden). Der Gesamtumfang des Moduls beträgt bei 6 Creditpoints 180 Stunden (30 Stunden/ECTS Punkt). Daher stehen für die Vor- und Nachbereitung der Veranstaltung zusammen mit der Prüfungsvorbereitung 135 Stunden zur Verfügung.
PIM-DS
Data Science
Prof. Dr. Klaus Berberich
kbe
Prof. Dr. Klaus Berberich
kbe
Students know about fundamental of deep neural networks and how they can be used to address various tasks in different domains (e.g., Natural Language Processing and Computer Vision). Students obtain a solid understanding of the theoretical underpinnings of deep neural networks such as optimization algorithms for learning parameters (e.g., variants of gradient descent) and activation functions (e.g., sigmoid, tanh, and ReLU). Given a specific task, students can put together a suitable neural network architecture (e.g., a CNN or RNN) and implement it using a standard framework (e.g., TensorFlow with Keras). Furthermore, students are aware of typical issues that can arise when training neural networks (e.g., overfitting) and know how to counteract them.
1. Introduction
2. Fundamentals of Machine Learning
3. Feed-Forward Neural Networks
4. Convolutional Neural Networks
5. Recurrent Neural Networks
6. Representation Learning
7. Generative Deep Learning
8. Outlook
F. Chollet: Deep Learning with Python,
Manning, 2018
I. Goodfellow, Y. Bengio, and A. Courville: Deep Learning,
MIT Press 2016
https://www.deeplearningbook.org
M. Nielsen: Neural Networks and Deep Learning,
Online, 2019
http://neuralnetworksanddeeplearning.com
A. Gulli, A. Kapoor, and S. Pal: Deep Learning with TensorFlow 2 and Keras,
Packt Publishing, 2019
H. Lane, C. Howard, and H. M. Hapke: Natural Language Processing in Action,
Manning, 2019
S. Raschka and V. Mirjalili: Python Machine Learning,
Packt Publishing, 2019
A. Burkov: The Hundred-Page Machine Learning Book,
self published, 2019
http://themlbook.com
SS 2024
SS 2023
SS 2022
SS 2021
SS 2020
Fri Mar 29 01:07:34 CET 2024, CKEY=kdl, BKEY=pim2, CID=[?], LANGUAGE=de, DATE=29.03.2024