https://datafranca.org/wiki/index.php?action=history&feed=atom&title=Initialisation_du_poids
Initialisation du poids - Historique des versions
2026-04-15T08:10:54Z
Historique des versions pour cette page sur le wiki
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https://datafranca.org/wiki/index.php?title=Initialisation_du_poids&diff=63232&oldid=prev
Pitpitt le 3 septembre 2022 à 13:52
2022-09-03T13:52:33Z
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Version du 3 septembre 2022 à 09:52</td>
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<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>[[Catégorie:<del style="font-weight: bold; text-decoration: none;">PUBLICATION</del>]]</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>[[Catégorie:<ins style="font-weight: bold; text-decoration: none;">GRAND LEXIQUE FRANÇAIS</ins>]]</div></td></tr>
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Pitpitt
https://datafranca.org/wiki/index.php?title=Initialisation_du_poids&diff=63195&oldid=prev
Imeziani : Imeziani a déplacé la page Weight Initialization vers Initialisation du poids
2022-09-01T17:48:33Z
<p>Imeziani a déplacé la page <a href="/wiki/Weight_Initialization" class="mw-redirect" title="Weight Initialization">Weight Initialization</a> vers <a href="/wiki/Initialisation_du_poids" title="Initialisation du poids">Initialisation du poids</a></p>
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<td colspan="1" style="background-color: #fff; color: #202122; text-align: center;">Version du 1 septembre 2022 à 13:48</td>
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Imeziani
https://datafranca.org/wiki/index.php?title=Initialisation_du_poids&diff=63194&oldid=prev
Imeziani le 1 septembre 2022 à 17:48
2022-09-01T17:48:07Z
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<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;">==en construction==</del></div></td><td colspan="2" class="diff-side-added"></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;"></del></div></td><td colspan="2" class="diff-side-added"></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Définition ==</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Définition ==</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;">XXXXXXXXX</del></div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">Procédure permettant de fixer les poids d'un réseau neuronal à de petites valeurs aléatoires qui définissent le point de départ de l'optimisation (apprentissage ou formation) du modèle de réseau neuronal.</ins></div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Français ==</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Français ==</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>''' <del style="font-weight: bold; text-decoration: none;">XXXXXXXXX </del>'''</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>''' <ins style="font-weight: bold; text-decoration: none;">initialisation du poids </ins>'''</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Anglais ==</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Anglais ==</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>''' <del style="font-weight: bold; text-decoration: none;">Weight Initialization</del>'''</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>''' <ins style="font-weight: bold; text-decoration: none;">weight initialization</ins>'''</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div> </div></td><td colspan="2" class="diff-side-added"></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;">The point of weight initialization is to make sure that a neural network doesn’t converge to a trivial solution.</del></div></td><td colspan="2" class="diff-side-added"></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;">If the weights are all initialized to the same value(eg. equal to zero) then each unit will get exactly the same signal and every layer would behave as if it were a single cell.</del></div></td><td colspan="2" class="diff-side-added"></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;">Therefore, you want to randomly initialize the weights near zero, but not equal to zero. This is an expectation of the stochastic optimization algorithm that’s used to train the model.</del></div></td><td colspan="2" class="diff-side-added"></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;"><small></del></div></td><td colspan="2" class="diff-side-added"></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>[https://<del style="font-weight: bold; text-decoration: none;">towardsdatascience</del>.com/<del style="font-weight: bold; text-decoration: none;">ten</del>-deep-learning-<del style="font-weight: bold; text-decoration: none;">concepts</del>-<del style="font-weight: bold; text-decoration: none;">you</del>-<del style="font-weight: bold; text-decoration: none;">should</del>-<del style="font-weight: bold; text-decoration: none;">know</del>-<del style="font-weight: bold; text-decoration: none;">for</del>-<del style="font-weight: bold; text-decoration: none;">data-science-interviews-a77f10bb9662 </del> Source : <del style="font-weight: bold; text-decoration: none;">towardsdatascience</del>]</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>[https://<ins style="font-weight: bold; text-decoration: none;">machinelearningmastery</ins>.com/<ins style="font-weight: bold; text-decoration: none;">weight-initialization-for</ins>-deep-learning-<ins style="font-weight: bold; text-decoration: none;">neural</ins>-<ins style="font-weight: bold; text-decoration: none;">networks/ Source : Machine Learning Mastery]</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">[https://www.linkedin.com/learning/tensorflow</ins>-<ins style="font-weight: bold; text-decoration: none;">pour</ins>-<ins style="font-weight: bold; text-decoration: none;">le</ins>-<ins style="font-weight: bold; text-decoration: none;">deep</ins>-<ins style="font-weight: bold; text-decoration: none;">learning </ins> Source : <ins style="font-weight: bold; text-decoration: none;">LikedIn Learning</ins>]</div></td></tr>
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<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>[[Catégorie:<del style="font-weight: bold; text-decoration: none;">vocabulary</del>]]</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>[[Catégorie:<ins style="font-weight: bold; text-decoration: none;">PUBLICATION</ins>]]</div></td></tr>
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Imeziani
https://datafranca.org/wiki/index.php?title=Initialisation_du_poids&diff=59102&oldid=prev
Pitpitt : Page créée avec « ==en construction== == Définition == XXXXXXXXX == Français == ''' XXXXXXXXX ''' == Anglais == ''' Weight Initialization''' The point of weight initialization is to m... »
2022-01-02T16:22:08Z
<p>Page créée avec « ==en construction== == Définition == XXXXXXXXX == Français == ''' XXXXXXXXX ''' == Anglais == ''' Weight Initialization''' The point of weight initialization is to m... »</p>
<p><b>Nouvelle page</b></p><div>==en construction==<br />
<br />
== Définition ==<br />
XXXXXXXXX<br />
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== Français ==<br />
''' XXXXXXXXX '''<br />
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== Anglais ==<br />
''' Weight Initialization'''<br />
<br />
The point of weight initialization is to make sure that a neural network doesn’t converge to a trivial solution.<br />
If the weights are all initialized to the same value(eg. equal to zero) then each unit will get exactly the same signal and every layer would behave as if it were a single cell.<br />
Therefore, you want to randomly initialize the weights near zero, but not equal to zero. This is an expectation of the stochastic optimization algorithm that’s used to train the model.<br />
<small><br />
<br />
[https://towardsdatascience.com/ten-deep-learning-concepts-you-should-know-for-data-science-interviews-a77f10bb9662 Source : towardsdatascience]<br />
<br />
<br />
[[Catégorie:vocabulary]]</div>
Pitpitt