Es ist eine neuere Version 2021W dieser LV im Curriculum Master's programme Computational Mathematics 2023W vorhanden.
Workload
Education level
Study areas
Responsible person
Hours per week
Coordinating university
3 ECTS
M1 - Master's programme 1. year
Computer Science
Sepp Hochreiter
2 hpw
Johannes Kepler University Linz
Detailed information
Original study plan
Master's programme Artificial Intelligence 2019W
Objectives
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.
Subject
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)
