Bachelor Thesis Open Access
Heine, Greta Sophie
<?xml version='1.0' encoding='UTF-8'?> <record xmlns="http://www.loc.gov/MARC21/slim"> <leader>00000nam##2200000uu#4500</leader> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">user-computing</subfield> </datafield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">user-etp</subfield> </datafield> <datafield tag="100" ind1=" " ind2=" "> <subfield code="a">Heine, Greta Sophie</subfield> <subfield code="u">KIT/ETP</subfield> </datafield> <datafield tag="542" ind1=" " ind2=" "> <subfield code="l">open</subfield> </datafield> <datafield tag="260" ind1=" " ind2=" "> <subfield code="c">2019-11-04</subfield> </datafield> <datafield tag="909" ind1="C" ind2="O"> <subfield code="o">oai:publish.etp.kit.edu:21984</subfield> <subfield code="p">user-computing</subfield> <subfield code="p">user-etp</subfield> </datafield> <datafield tag="245" ind1=" " ind2=" "> <subfield code="a">Illustration of the Neural Network Learning Process during Training</subfield> </datafield> <datafield tag="856" ind1="4" ind2=" "> <subfield code="s">7743959</subfield> <subfield code="u">https://publish.etp.kit.edu/record/21984/files/BA_greta_heine.pdf</subfield> <subfield code="z">md5:5ad0a4cc8d8f3ee063123e8878fe5ac5</subfield> </datafield> <controlfield tag="001">21984</controlfield> <datafield tag="041" ind1=" " ind2=" "> <subfield code="a">eng</subfield> </datafield> <controlfield tag="005">20200825130929.0</controlfield> <datafield tag="653" ind1=" " ind2=" "> <subfield code="a">neural network, training, Taylor expansion</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Wolf, Roger</subfield> <subfield code="u">KIT/ETP</subfield> <subfield code="4">ths</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Quast, Guenther</subfield> <subfield code="u">KIT/ETP</subfield> <subfield code="4">ths</subfield> </datafield> <datafield tag="520" ind1=" " ind2=" "> <subfield code="a"><p>This thesis enhances the understanding of Neural Network (NN) trainings by<br> investigation of the learning process especially focusing on the dependence<br> of the NN output on the input space for given tasks. For this purpose, the<br> NN function is decomposed into a Taylor expansion. The Taylor coefficients<br> serve as a metric to illustrate the influence of input space features on the<br> output at each step of the training. Both, the arithmetic mean values of the<br> Taylor coefficients and their dependence on each point of the input space are<br> investigated, giving new insights into the decision taking of NNs.</p></subfield> </datafield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">thesis</subfield> <subfield code="b">bachelor-thesis</subfield> </datafield> </record>