Special Lecture by Prof. Dr. Gregor Kasieczka

— abgelegt unter:

Basics of Deep Learning & Applications in Particle Physics

Was
  • Lecture
Wann 09.04.2018 09:30 bis
10.04.2018 12:00
Wo Hörsaal I, Physikalisches Institut
Name
Kontakttelefon 0761-203 5715
Teilnehmer PhD students of the RTG 2044 and external students
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Abstract:

Deep learning algorithms nowadays outperform humans in image classification tasks, beat them at challenging games such as Go or Poker, respond to spoken questions, and might soon drive autonomous cars. These achievements were made possible by faster hardware and by using the parallel processing capabilities of modern graphical processing units (GPUs), but at least equally important were developments in software.

Deep learning is the application of many-layered (deep) artificial neural networks. We will start by discussing the essential building blocks, such as activation functions, loss functions, back propagation and gradient descent using different optimiser algorithms. A tour of different deep learning approaches used for classification tasks in particle physics provides an overview of common network architectures. We address strategies how networks can be trained directly on data to rely less on computer simulations and how one can achieve a deeper understanding of the decision process inside the network. Useful concepts such as generative adversarial networks and auto encoders are discussed. Finally, we look at software libraries for implementing deep neural networks.

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