In Alma-Vorlesungsverzeichnis

Description:

The lecture will cover the main principles and models of "deep neural networks", adapted from the textbook "Deep Learning" by Goodfellow, Bengio and Courville. These network models are currently top performers in recognition for many machine learning problems, from object recognition, image segmentation, speech recognition, handwriting recognition, to time series analysis. They are used for autonomous driving, by internet companies for semantic labeling of image data, and in mobile assistants such as Siri, Cortana, and Google Now.

After an introduction to the topic, deep neural networks and techniques such as backpropagation are covered; followed by regularization techniques for deep neural networks (which improve their generalization performance on unknown test data). This is followed by optimization techniques for training deep neural networks and a chapter on convolutional neural networks (CNNs). Further chapters on recurrent networks and autoencoders follow. At the end, very recent architectures like transformers, large language models, generative models and applications will be treated.

In the exercises, the theoretical knowledge acquired in the lecture will also be deepened by theoretical questions and practical tasks. In addition to programming tasks in Python, the deep learning library PyTorch from Facebook will be used.

Requirements:

The lecture is intended for master students!
Good knowledge of mathematics I - III (linear algebra, statistics, numerics) is required.
The material is very extensive and contains many formulas (and too much text on the slides).
Python programming skills are assumed.

Registration (Mandatory):

Via Moodle: Deep Learning

Literatur:

• Lecture notes (will be made available as PDF in Moodle before the lecture)
• Ian Goodfellow, Yoshua Bengio, Aaron de Courville: Deep Learning, MIT Press, 2016.The book is available online on the WWW, cf.
• http://www.deeplearningbook.org/

Exercises for the lecture Deep Neural Networks