chore(i18n,curriculum): update translations (#42943)

curriculum/challenges/italian/11-machine-learning-with-python/how-neural-networks-work/deep-learning-demystified.md

@@ -1,6 +1,6 @@
 ---
 id: 5e9a0e9ef99a403d019610cc
-title: Deep Learning Demystified
+title: Deep learning demistificato
 challengeType: 11
 videoId: bejQ-W9BGJg
 dashedName: deep-learning-demystified
@@ -10,23 +10,23 @@ dashedName: deep-learning-demystified
 
 ## --text--
 
-How should you assign weights to input neurons before training your network for the first time?
+Come dovresti assegnare i pesi ai neuroni in ingresso prima di addestrare la rete per la prima volta?
 
 ## --answers--
 
-From smallest to largest.
+Dal più piccolo al più grande.
 
 ---
 
-Completely randomly.
+In modo completamente casuale.
 
 ---
 
-Alphabetically.
+Alfabeticamente.
 
 ---
 
-None of the above.
+Niente di quanto sopra.
 
 ## --video-solution--
 

curriculum/challenges/italian/11-machine-learning-with-python/how-neural-networks-work/how-convolutional-neural-networks-work.md

@@ -1,6 +1,6 @@
 ---
 id: 5e9a0e9ef99a403d019610cd
-title: How Convolutional Neural Networks work
+title: Come funzionano le reti neurali convoluzionali
 challengeType: 11
 videoId: Y5M7KH4A4n4
 dashedName: how-convolutional-neural-networks-work
@@ -10,19 +10,19 @@ dashedName: how-convolutional-neural-networks-work
 
 ## --text--
 
-When are Convolutional Neural Networks not useful?
+Quando le reti neurali convoluzionali non sono utili?
 
 ## --answers--
 
-If your data can't be made to look like an image, or if you can rearrange elements of your data and it's still just as useful.
+Se i tuoi dati non possono essere fatti sembrare un'immagine, o se è possibile riorganizzare elementi dei tuoi dati ed è ancora altrettanto utile.
 
 ---
 
-If your data is made up of different 2D or 3D images.
+Se i tuoi dati sono costituiti da diverse immagini 2D o 3D.
 
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-If your data is text or sound based.
+Se i tuoi dati sono basati su testo o suono.
 
 ## --video-solution--
 

curriculum/challenges/italian/11-machine-learning-with-python/how-neural-networks-work/how-deep-neural-networks-work.md

@@ -1,6 +1,6 @@
 ---
 id: 5e9a0e9ef99a403d019610ca
-title: How Deep Neural Networks Work
+title: Come funzionano le reti neurali profonde
 challengeType: 11
 videoId: zvalnHWGtx4
 dashedName: how-deep-neural-networks-work
@@ -10,19 +10,19 @@ dashedName: how-deep-neural-networks-work
 
 ## --text--
 
-Why is it better to calculate the gradient (slope) directly rather than numerically?
+Perché è meglio calcolare il gradiente (pendenza) direttamente piuttosto che numericamente?
 
 ## --answers--
 
-It is computationally expensive to go back through the entire neural network and adjust the weights for each layer of the neural network.
+È computazionalmente costoso passare attraverso l'intera rete neurale e regolare il peso per ogni strato della rete neurale.
 
 ---
 
-It is more accurate.
+È più preciso.
 
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-There is no difference between the two methods.
+Non vi è alcuna differenza tra i due metodi.
 
 ## --video-solution--
 

curriculum/challenges/italian/11-machine-learning-with-python/how-neural-networks-work/recurrent-neural-networks-rnn-and-long-short-term-memory-lstm.md

@@ -1,6 +1,6 @@
 ---
 id: 5e9a0e9ef99a403d019610cb
-title: Recurrent Neural Networks RNN and Long Short Term Memory LSTM
+title: Reti neurali ricorrenti RNN e memoria a lungo breve termine LSTM
 challengeType: 11
 videoId: UVimlsy9eW0
 dashedName: recurrent-neural-networks-rnn-and-long-short-term-memory-lstm
@@ -10,19 +10,19 @@ dashedName: recurrent-neural-networks-rnn-and-long-short-term-memory-lstm
 
 ## --text--
 
-What are the main neural network components that make up a Long Short Term Memory network?
+Quali sono i principali componenti di reti neurali che compongono una rete a Memoria a Lungo Breve Termine (Long Short Term Memory - LSTM)?
 
 ## --answers--
 
-New information and prediction.
+Nuove informazioni e predizioni.
 
 ---
 
-Prediction, collected possibilities, and selection.
+Predizioni, collezioni di possibilità e selezioni.
 
 ---
 
-Prediction, ignoring, forgetting, and selection.
+Predizioni, ignorare, dimenticare, e selezioni.
 
 ## --video-solution--