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Marco Realacci 2024-10-29 13:21:43 +01:00
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commit 94637a29d1
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.obsidian/workspace.json vendored
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@ -190,7 +190,7 @@
"smart-second-brain:Open S2B Chat": false "smart-second-brain:Open S2B Chat": false
} }
}, },
"active": "5af0211cef5316b5", "active": "0d5325c0f9289cea",
"lastOpenFiles": [ "lastOpenFiles": [
"Foundation of data science/images/Pasted image 20241029123613.png", "Foundation of data science/images/Pasted image 20241029123613.png",
"Foundation of data science/notes/2 Logistic Regression.md", "Foundation of data science/notes/2 Logistic Regression.md",

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Foundation of data science/notes/2 Logistic Regression.md
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@ -66,6 +66,6 @@ Ora tutto ciò lo applichiamo alla LL (log likelihood):
$$\theta^{t+1} = \theta^{t} - \frac{LL'(\theta^0)}{LL''(\theta^0)}$$ $$\theta^{t+1} = \theta^{t} - \frac{LL'(\theta^0)}{LL''(\theta^0)}$$
fidatevi, funziona. fidatevi, funziona.
Se $\theta$ è un vettore, possiamo anche scriverlo così: $$\theta^{t+1} = \theta^{t} - \frac{\nabla_{\theta}LL}{H}$$ Se $\theta$ è un vettore, possiamo anche scriverlo così: $$\theta^{t+1} = \theta^{t} - H^{-1}\nabla LL(\theta)$$
con H matrice definita come segue: $$H_{i,j}=\frac{\partial^2 LL}{\partial \Theta_i \partial \Theta_j} con H matrice definita come segue: $$H_{i,j}=\frac{\partial^2 LL}{\partial \Theta_i \partial \Theta_j}
$$ $$