http://ricefriedegg.com:80/mediawiki/index.php?action=history&feed=atom&title=Activation_function 2026-05-26T22:22:05Z Revision history for this page on the wiki MediaWiki 1.41.0 http://ricefriedegg.com:80/mediawiki/index.php?title=Activation_function&diff=639&oldid=prev 2024-05-01T00:15:44Z

<p></p> <table style="background-color: #fff; color: #202122;" data-mw="interface"> <col class="diff-marker" /> <col class="diff-content" /> <col class="diff-marker" /> <col class="diff-content" /> <tr class="diff-title" lang="en"> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">← Older revision</td> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 00:15, 1 May 2024</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l2">Line 2:</td> <td colspan="2" class="diff-lineno">Line 2:</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>The choice of activation function varies depending on the machine learning task. A simple linear activation function or no activation function is just [[linear regression]]. A sigmoid function can be used for a classification task.</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>The choice of activation function varies depending on the machine learning task. A simple linear activation function or no activation function is just [[linear regression]]. A sigmoid function can be used for a classification task.</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;"></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;">[[Category:Machine Learning]]</ins></div></td></tr> <!-- diff cache key my_wiki:diff:1.41:old-638:rev-639:php=table --> </table>

Rice http://ricefriedegg.com:80/mediawiki/index.php?title=Activation_function&diff=638&oldid=prev 2024-05-01T00:15:31Z

<p>Created page with &quot;The &#039;&#039;&#039;activation function&#039;&#039;&#039; determines the output of a neuron in a <a href="/mediawiki/index.php/Neural_network" title="Neural network">neural network</a>. It generates the output of the neuron from the linear combination of inputs calculated by the neuron. The choice of activation function varies depending on the machine learning task. A simple linear activation function or no activation function is just <a href="/mediawiki/index.php/Linear_regression" title="Linear regression">linear regression</a>. A sigmoid function can be used for a classification task.&quot;</p> <p><b>New page</b></p><div>The &#039;&#039;&#039;activation function&#039;&#039;&#039; determines the output of a neuron in a [[neural network]]. It generates the output of the neuron from the linear combination of inputs calculated by the neuron.<br /> <br /> The choice of activation function varies depending on the machine learning task. A simple linear activation function or no activation function is just [[linear regression]]. A sigmoid function can be used for a classification task.</div>

Rice