11 - Machine Learning for Physicists 2019

This is a course introducing modern techniques of machine learning, especially deep neural networks, to an audience of physicists. Neural networks can be trained to perform diverse challenging tasks, including image recognition and natural language processing, just by training them on many examples. Neural networks have recently achieved spectacular successes, with their performance often surpassing humans. They are now also being considered more and more for applications in physics, ranging from predictions of material properties to analyzing phase transitions. We will cover the basics of neural networks, convolutional networks, autoencoders, restricted Boltzmann machines, and recurrent neural networks, as well as the recently emerging applications in physics.

Share 11 - Machine Learning for Physicists 2019

Sign up to save your podcasts

11 - Machine Learning for Physicists 2019

11 - Machine Learning for Physicists 2019

More shows like Machine Learning for Physicists 2019 (QHD 1920)

Quantum-optical phenomena in nanophysics 2010 (SD 640)

Pattern Recognition PR 2012/2013 (SD 640)

Foundations of Quantum Mechanics 2013 (SD 640)

Lectures on the Geometric Anatomy of Theoretical Physics 2013/2014 (SD 640)

Lineare Algebra I 2014/2015 (Audio)

Lectures on Quantum Theory (Elite Graduate Programme) 2015 -Measurements (HD 1280)

Modern Optics 3: Quantum Optics 2019/2020 (QHD 1920)

Quantum Computing 2019/2020 (QHD 1920)

ASC - Advanced Signal Processing and Communications Engineering (QHD 1920)

Advanced Design and Programming 2019/2020 (QHD 1920 - Video & Folien)