Course Information
The lecture will provide an introduction to the practical implementation of Deep Learning architectures and algorithms. We will discuss the Statistical Mechanics approach to understand the general principles which underly the success of such algorithms.
In particular, we will discuss the structure of deep neural networks and the back-propagation algorithm through minimization of a coast function. We use the MNIST dataset as an example to train different neural networks architectures using gradient descent. Furthermore, we will study the analysis of Gibbs and online learning of a perceptron in the teacher-student configuration, calculation of quenched averages using the replica method, analysis of two-layer networks using the Committee Machine as an example, random matrix theory, bias-variance trade-off, analysis of weight matrices and the application of neural networks to solve physical problems. An introduction to the use of TensorFlow and Keras will be given. | |
Prof. Rosenow, Thursday 15:15-16:45 and Friday 11:0 -12:30, (Lecture) | |
Assem Afanah, Thursday 09:15-10:45, (Tutorial) | |
Module Registration is possible via email
einschreibung-physgeo@uni-leipzig.de 10 LP (credit points), 300 h workload |
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Written examination (180 min) with weighting 1,
Pre-requisite: 50% of the homework points |
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The introductory part of the lecture will follow the book by Michael Nielsen, Neural Networks and Deep Learning. Other parts of the lecture will be based on the book "Statistical Mechanics of Learning" by A. Engel and C. Van den Broeck, which is available in the library. | |
For beginners with Python, we recommend the introductory course Introduction to Computer-based Physical Modeling by Frank Cichos. | |
preliminary results | |
For any questions about the course, Please contact the tutor of the class, Assem Afanah
afanah@itp.uni-leipzig.de |
Problem Sets and Lecture Notes
Problem sets will be posted online every week on Monday. Solutions must be mailed as single pdf file before Tuesday 23:59 to stp2.leipziguni@gmail.com