Add neural network part
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2 changed files with 349 additions and 1 deletions
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content/ia/3_deep_learning/images/transformation.svg
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content/ia/3_deep_learning/images/transformation.svg
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id="path12628" />
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<path
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style="fill:none;fill-opacity:1;stroke:#b183f5;stroke-width:0.365;stroke-dasharray:none;stroke-opacity:1;marker-end:url(#marker5385)"
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d="m 69.966949,37.999178 c -0.06465,7.178468 31.190761,7.329311 30.999211,0.116384"
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id="path17538"
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sodipodi:nodetypes="cc" />
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<circle
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style="fill:#ffffff;fill-opacity:1;stroke:#b183f5;stroke-width:0.365001;stroke-dasharray:none;stroke-opacity:1"
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id="circle17540"
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cx="85.661873"
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cy="43.221676"
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r="3.3143868" />
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aria-label="1"
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transform="translate(2.1166667,-8.4666671)">
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<path
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transform="translate(2.1166667,-8.4666671)">
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<path
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</svg>
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After Width: | Height: | Size: 13 KiB |
|
|
@ -99,10 +99,38 @@ D'un neurone vers l'infini...
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
Dans un réseau de neurones, chacune a son propre poid et biais, les paramètres
|
Dans un réseau de neurones, chacun a son propre poid et biais, les paramètres
|
||||||
du réseau sont composés de l'ensemble des poids et biais. Tous les neurones
|
du réseau sont composés de l'ensemble des poids et biais. Tous les neurones
|
||||||
d'une même couche ont **les mêmes fonctions**.
|
d'une même couche ont **les mêmes fonctions**.
|
||||||
|
|
||||||
|
Le réseaux est organisés en couche. Les **reseaux de neuronnes complètement
|
||||||
|
connectés**, toutes les sorties des neuronnes de la couche 0 sont connectés aux
|
||||||
|
entrées de tous les neurines de la couche 1.
|
||||||
|
|
||||||
|
### Application de transformation
|
||||||
|
|
||||||
|
Les résultats obtenus en sortie de notre réseaux noe sont pas forcément
|
||||||
|
compatibles avec la séparation linéaires. Il est alors possible d'appliquer une
|
||||||
|
**matrice de transformation**, ce qui reviens à appliquer une autre couche à
|
||||||
|
notre réseau.
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
Dans le schéma ci-dessus:
|
||||||
|
|
||||||
|
1. La première transformation est \\(
|
||||||
|
W = \begin{pmatrix}
|
||||||
|
+\frac{1}{2} -\frac{1}{2} \\\
|
||||||
|
-\frac{1}{2} +\frac{1}{2}
|
||||||
|
\end{pmatrix}
|
||||||
|
\\) ce qu nous donne \\(Wx\\)
|
||||||
|
2. La seconde est \\(g(u) = max\\{0,x\\}\\) ce qu nous donne \\(h(x) = g(Wx)\\)
|
||||||
|
|
||||||
|
Au final, nous avons \\({w_{2}}^{t}h(x)+ b_{2} = (4 4)h(x) - 1 \implies y = {w_{2}}^{t}h(x)+ b_{2} \\)
|
||||||
|
|
||||||
|
Ce n'est rien d'autre qu'un réseaux de neurones avec une couche cachée \\(h\\)
|
||||||
|
|
||||||
|
|
||||||
[wiki_neurone]:https://commons.wikimedia.org/w/index.php?title=User:Nicolas.Rougier
|
[wiki_neurone]:https://commons.wikimedia.org/w/index.php?title=User:Nicolas.Rougier
|
||||||
[mcculloch]:https://fr.wikipedia.org/wiki/Warren_McCulloch
|
[mcculloch]:https://fr.wikipedia.org/wiki/Warren_McCulloch
|
||||||
[pitts]:https://fr.wikipedia.org/wiki/Walter_Pitts
|
[pitts]:https://fr.wikipedia.org/wiki/Walter_Pitts
|
||||||
|
|
|
||||||
Loading…
Add table
Add a link
Reference in a new issue