Es ist eine neuere Version 2021W dieser LV im Curriculum Masterstudium Computational Mathematics 2023W vorhanden.
(*) Leider ist diese Information in Deutsch nicht verfügbar.
Workload
Ausbildungslevel
Studienfachbereich
VerantwortlicheR
Semesterstunden
Anbietende Uni
3 ECTS
M1 - Master 1. Jahr
Informatik
Sepp Hochreiter
2 SSt
Johannes Kepler Universität Linz
Detailinformationen
Quellcurriculum
Masterstudium Artificial Intelligence 2019W
Ziele
(*)In this class, students will learn about Recurrent neural networks (RNNs) and Long Short Term Memory (LSTM), types of neural networks that are very commonly used for predicting sequential data, such as time-series, text or DNA. We will cover both theoretical fundamentals and advanced applications. It is expected that students visiting this class already have a solid understanding of deep learning.
Lehrinhalte
(*)
Basics of sequence prediction
Recurrent neural networks
Vanishing Gradients and LSTMs
Attention Models
Memory Models (e.g. Neural Turing Machine)
Autoregressive Sequence Models and Transformers (PixelRNN, ByteNet, Transformers)
Applications in NLP (e.g. next character prediction, language modelling, sentiment analysis)
