clara/pytorch-ai
1
0
Fork 0
Code Issues 1 Pull requests Projects Releases Packages Wiki Activity Actions

Net definition section finished

Browse source
This commit is contained in:
Clemens Dautermann 2020-01-30 16:12:32 +01:00
parent 4b80e7391f
commit b061fd5a2f
17 changed files with 221 additions and 56 deletions
Download patch file Download diff file Expand all files Collapse all files

46
doc/Grundlagen_des_maschinellen_lernens.aux
View file

@ -108,23 +108,28 @@
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {4.1}Datenvorbereitung}{22}{subsection.4.1}\protected@file@percent }
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {27}{\ignorespaces Der Code zum Laden des MNIST Datensatzes\relax }}{22}{figure.caption.25}\protected@file@percent }
\newlabel{MNIST_Dataloader_Code}{{27}{22}{Der Code zum Laden des MNIST Datensatzes\relax }{figure.caption.25}{}}
\abx@aux@cite{6}
\abx@aux@segm{0}{0}{6}
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {4.2}Definieren des Netzes}{23}{subsection.4.2}\protected@file@percent }
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {4.3}Trainieren des Netzes}{24}{subsection.4.3}\protected@file@percent }
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {section}{\numberline {5}Fallbeispiel I:\newline Ein Klassifizierungsnetzwerk f\IeC {\"u}r handgeschriebene Ziffern}{24}{section.5}\protected@file@percent }
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {5.1}Aufgabe}{24}{subsection.5.1}\protected@file@percent }
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {5.2}Der MNIST Datensatz}{24}{subsection.5.2}\protected@file@percent }
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {5.3}Ergebnis}{24}{subsection.5.3}\protected@file@percent }
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {section}{\numberline {6}Fallbeispiel II:\newline Eine selbsttrainierende KI f\IeC {\"u}r Tic-Tac-Toe}{24}{section.6}\protected@file@percent }
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {6.1}Das Prinzip}{24}{subsection.6.1}\protected@file@percent }
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {6.2}Chance-Tree Optimierung}{24}{subsection.6.2}\protected@file@percent }
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {6.3}L\IeC {\"o}sung mittels eines neuronalen Netzes}{24}{subsection.6.3}\protected@file@percent }
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {6.4}Vergleich}{24}{subsection.6.4}\protected@file@percent }
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {section}{\numberline {7}Schlusswort}{24}{section.7}\protected@file@percent }
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {28}{\ignorespaces Code um ein einfaches Netz in Pytorch zu definieren\relax }}{24}{figure.caption.26}\protected@file@percent }
\newlabel{Net_simple_definition}{{28}{24}{Code um ein einfaches Netz in Pytorch zu definieren\relax }{figure.caption.26}{}}
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {4.3}Trainieren des Netzes}{25}{subsection.4.3}\protected@file@percent }
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {section}{\numberline {5}Fallbeispiel I:\newline Ein Klassifizierungsnetzwerk f\IeC {\"u}r handgeschriebene Ziffern}{25}{section.5}\protected@file@percent }
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {5.1}Aufgabe}{25}{subsection.5.1}\protected@file@percent }
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {5.2}Der MNIST Datensatz}{25}{subsection.5.2}\protected@file@percent }
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {5.3}Ergebnis}{25}{subsection.5.3}\protected@file@percent }
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {section}{\numberline {6}Fallbeispiel II:\newline Eine selbsttrainierende KI f\IeC {\"u}r Tic-Tac-Toe}{25}{section.6}\protected@file@percent }
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {6.1}Das Prinzip}{25}{subsection.6.1}\protected@file@percent }
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {6.2}Chance-Tree Optimierung}{25}{subsection.6.2}\protected@file@percent }
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {6.3}L\IeC {\"o}sung mittels eines neuronalen Netzes}{25}{subsection.6.3}\protected@file@percent }
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {6.4}Vergleich}{25}{subsection.6.4}\protected@file@percent }
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {section}{\numberline {7}Schlusswort}{25}{section.7}\protected@file@percent }
\bibcite{1}{1}
\bibcite{2}{2}
\bibcite{3}{3}
\bibcite{4}{4}
\bibcite{5}{5}
\bibcite{6}{6}
\gdef\minted@oldcachelist{,
default-pyg-prefix.pygstyle,
default.pygstyle,
@ -137,6 +142,21 @@
FAA14B25340C2458FD5D369A042116ABF6C426F58C5CCC27D3C7BD698FEC22DB.pygtex,
745405EA92794205A61C3062FFF26B3CF6C426F58C5CCC27D3C7BD698FEC22DB.pygtex,
2310AA0C124794A026E719F78EC1B44DF6C426F58C5CCC27D3C7BD698FEC22DB.pygtex,
81326AEB166BABDF0E8E9CFEEFD02903F6C426F58C5CCC27D3C7BD698FEC22DB.pygtex,
EDB0BACF1F2CD448B8D22F1A9B4F026EF6C426F58C5CCC27D3C7BD698FEC22DB.pygtex,
9A791CC5219F6F4D627591113AE747DBF6C426F58C5CCC27D3C7BD698FEC22DB.pygtex,
090D83D976E5CEECA0A0961BB3B3A5A6F6C426F58C5CCC27D3C7BD698FEC22DB.pygtex}
090D83D976E5CEECA0A0961BB3B3A5A6F6C426F58C5CCC27D3C7BD698FEC22DB.pygtex,
12F96C5C5DB151598C41F68BDCE383E2F6C426F58C5CCC27D3C7BD698FEC22DB.pygtex,
7C08F8DDD5F5D41169EB51C2ADB3E0B1F6C426F58C5CCC27D3C7BD698FEC22DB.pygtex,
D6B06E011A4BC083034B9BD6480B9AB253A0C9FE66949F8EC4BED65B31F6975B.pygtex,
7572134CCA2BA7D050325F33C4941E41F6C426F58C5CCC27D3C7BD698FEC22DB.pygtex,
689375EA3B9C38D437AA368912E13885F6C426F58C5CCC27D3C7BD698FEC22DB.pygtex,
B68FDDAFFF09CEFEB070EDD4AD4C96E1F6C426F58C5CCC27D3C7BD698FEC22DB.pygtex,
B68FDDAFFF09CEFEB070EDD4AD4C96E1F6C426F58C5CCC27D3C7BD698FEC22DB.pygtex,
689375EA3B9C38D437AA368912E13885F6C426F58C5CCC27D3C7BD698FEC22DB.pygtex,
7C08F8DDD5F5D41169EB51C2ADB3E0B1F6C426F58C5CCC27D3C7BD698FEC22DB.pygtex,
7572134CCA2BA7D050325F33C4941E41F6C426F58C5CCC27D3C7BD698FEC22DB.pygtex,
7572134CCA2BA7D050325F33C4941E41F6C426F58C5CCC27D3C7BD698FEC22DB.pygtex,
689375EA3B9C38D437AA368912E13885F6C426F58C5CCC27D3C7BD698FEC22DB.pygtex,
6361598B18370336863B24D1B6FAE96EF6C426F58C5CCC27D3C7BD698FEC22DB.pygtex,
7C08F8DDD5F5D41169EB51C2ADB3E0B1F6C426F58C5CCC27D3C7BD698FEC22DB.pygtex,
5A0B7618927FB8AEA4B96F5552228CD8F6C426F58C5CCC27D3C7BD698FEC22DB.pygtex}