# Reading material

**Books**

A very nice and gentle introduction (based on TensorFlow and Keras) is provided in the textbook *Deep Learning for Physics Research* by [Martin Erdmann](https://www.physik.rwth-aachen.de/user/erdmann), [Jonas Glombitza](https://www.jonas-glombitza.com/), [Gregor Kasieczka](https://www.physik.uni-hamburg.de/iexp/gruppe-kasieczka.html), and Uwe Klemradt.

{% embed url="<https://worldscientific.com/worldscibooks/10.1142/12294>" %}

You can find the excercises to the book (for free) here:

{% embed url="<http://www.deeplearningphysics.org>" %}

**Papers**

Eur. Phys. J. C 79 (2019): *ATLAS b-jet identification performance and efficiency measurement with tt events in pp collisions at √s = 13 TeV*

{% embed url="<https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/FTAG-2018-01/>" %}

ATL-PHYS-PUB-2020-01&#x34;**:** *Deep Sets based Neural Networks for Impact Parameter Flavour Tagging in ATLAS*

{% embed url="<https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2020-014/>" %}

ATL-PHYS-PUB-2017-010: *Variable Radius, Exclusive-kT, and Center-of-Mass Subjet Reconstruction for Higgs(→bb) Tagging in ATLAS*

{% embed url="<https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2017-010/>" %}

**Presentations**

Overview of flavour tagging algorithms in ATLAS

{% file src="<https://2285457888-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F4HmAFHZbBPboWBfsmAPj%2Fuploads%2FXEaCodQjeknVWuLld4EA%2FFTAG-summie-overview.pdf?alt=media&token=d90c81bf-ec60-4cea-bac1-ef842a0fcbce>" %}


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