From 2056db02996f2d7af1048d71a04dc8c6e8111793 Mon Sep 17 00:00:00 2001 From: camperbot Date: Tue, 20 Jul 2021 21:35:24 +0530 Subject: [PATCH] chore(i18n,curriculum): update translations (#42943) --- ...yle-until-a-condition-is-met-with-while.md | 28 +++++++++---------- .../deep-learning-demystified.md | 12 ++++---- .../how-convolutional-neural-networks-work.md | 10 +++---- .../how-deep-neural-networks-work.md | 10 +++---- ...rks-rnn-and-long-short-term-memory-lstm.md | 10 +++---- .../book-recommendation-engine-using-knn.md | 14 +++++----- .../cat-and-dog-image-classifier.md | 12 ++++---- ...near-regression-health-costs-calculator.md | 14 +++++----- .../neural-network-sms-text-classifier.md | 12 ++++---- ...al-neural-networks-evaluating-the-model.md | 10 +++---- ...ral-networks-picking-a-pretrained-model.md | 4 +-- ...neural-networks-the-convolutional-layer.md | 10 +++---- .../convolutional-neural-networks.md | 10 +++---- ...-learning-algorithms-building-the-model.md | 4 +-- ...core-learning-algorithms-classification.md | 10 +++---- .../core-learning-algorithms-clustering.md | 14 +++++----- ...earning-algorithms-hidden-markov-models.md | 10 +++---- ...earning-algorithms-the-training-process.md | 10 +++---- ...ng-algorithms-training-and-testing-data.md | 10 +++---- ...using-probabilities-to-make-predictions.md | 4 +-- ...e-learning-algorithms-working-with-data.md | 10 +++---- .../tensorflow/core-learning-algorithms.md | 14 +++++----- ...creating-a-convolutional-neural-network.md | 4 +-- ...roduction-machine-learning-fundamentals.md | 10 +++---- .../tensorflow/introduction-to-tensorflow.md | 10 +++---- ...processing-with-rnns-building-the-model.md | 4 +-- ...ssing-with-rnns-create-a-play-generator.md | 4 +-- ...processing-with-rnns-making-predictions.md | 10 +++---- ...al-language-processing-with-rnns-part-2.md | 10 +++---- ...ing-with-rnns-recurring-neural-networks.md | 12 ++++---- ...ocessing-with-rnns-sentimental-analysis.md | 4 +-- ...processing-with-rnns-training-the-model.md | 4 +-- .../natural-language-processing-with-rnns.md | 10 +++---- .../neural-networks-activation-functions.md | 8 +++--- .../neural-networks-creating-a-model.md | 4 +-- .../tensorflow/neural-networks-optimizers.md | 10 +++---- .../neural-networks-with-tensorflow.md | 10 +++---- ...cement-learning-with-q-learning-example.md | 4 +-- ...rcement-learning-with-q-learning-part-2.md | 8 +++--- .../reinforcement-learning-with-q-learning.md | 10 +++---- ...t-query-parameter-input-from-the-client.md | 12 ++++---- 41 files changed, 195 insertions(+), 195 deletions(-) diff --git a/curriculum/challenges/espanol/03-front-end-libraries/sass/apply-a-style-until-a-condition-is-met-with-while.md b/curriculum/challenges/espanol/03-front-end-libraries/sass/apply-a-style-until-a-condition-is-met-with-while.md index 0e6f582247..22b4fd934d 100644 --- a/curriculum/challenges/espanol/03-front-end-libraries/sass/apply-a-style-until-a-condition-is-met-with-while.md +++ b/curriculum/challenges/espanol/03-front-end-libraries/sass/apply-a-style-until-a-condition-is-met-with-while.md @@ -1,6 +1,6 @@ --- id: 587d7dbf367417b2b2512bbb -title: Apply a Style Until a Condition is Met with @while +title: Aplica un estilo hasta que se cumpla una condición con @while challengeType: 0 forumTopicId: 301454 dashedName: apply-a-style-until-a-condition-is-met-with-while @@ -8,9 +8,9 @@ dashedName: apply-a-style-until-a-condition-is-met-with-while # --description-- -The `@while` directive is an option with similar functionality to the JavaScript `while` loop. It creates CSS rules until a condition is met. +La directiva `@while` es una opción con funcionalidad similar al bucle `while` de JavaScript. Crea reglas CSS hasta que se cumpla una condición. -The `@for` challenge gave an example to create a simple grid system. This can also work with `@while`. +El desafío `@for` dio un ejemplo para crear un sistema de cuadrícula (grid) simple. Esto también puede funcionar con `@while`. ```scss $x: 1; @@ -20,59 +20,59 @@ $x: 1; } ``` -First, define a variable `$x` and set it to 1. Next, use the `@while` directive to create the grid system *while* `$x` is less than 13. After setting the CSS rule for `width`, `$x` is incremented by 1 to avoid an infinite loop. +Primero, define una variable `$x` y establécela a 1. A continuación, utiliza la directiva `@while` para crear el sistema de cuadrícula *while* `$x` sea menor que 13. Después de configurar la regla CSS para `width`, `$x` se incrementa por 1 para evitar un bucle infinito. # --instructions-- -Use `@while` to create a series of classes with different `font-sizes`. +Usa `@while` para crear una serie de clases con diferentes `font-sizes`. -There should be 5 different classes from `text-1` to `text-5`. Then set `font-size` to `15px` multiplied by the current index number. Make sure to avoid an infinite loop! +Debe haber 5 clases diferentes desde `text-1` hasta `text-5`. Luego ajusta `font-size` a `15px` multiplicado por el número de índice actual. ¡Asegúrate de evitar un bucle infinito! # --hints-- -Your code should use the `@while` directive. +El código debe usar la directiva `@while`. ```js assert(code.match(/@while /g)); ``` -Your code should use an index variable which starts at an index of 1. +El código debe utilizar una variable de índice que comience en un índice de 1. ```js assert(code.match(/\$.*:\s*?1;/gi)); ``` -Your code should increment the counter variable. +Tu código debe incrementar la variable contador. ```js assert(code.match(/\$(.*)\s*?:\s*\$\1\s*\+\s*1\s*;/gi)); ``` -Your `.text-1` class should have a `font-size` of `15px`. +Tu clase `.text-1` debe tener un `font-size` de `15px`. ```js assert($('.text-1').css('font-size') == '15px'); ``` -Your `.text-2` class should have a `font-size` of `30px`. +Tu clase `.text-2` debe tener un `font-size` de `30px`. ```js assert($('.text-2').css('font-size') == '30px'); ``` -Your `.text-3` class should have a `font-size` of `45px`. +Tu clase `.text-3` debe tener un `font-size` de `45px`. ```js assert($('.text-3').css('font-size') == '45px'); ``` -Your `.text-4` class should have a `font-size` of `60px`. +Tu clase `.text-4` debe tener un `font-size` de `60px`. ```js assert($('.text-4').css('font-size') == '60px'); ``` -Your `.text-5` class should have a `font-size` of `75px`. +Tu clase `.text-5` debe tener un `font-size` de `75px`. ```js assert($('.text-5').css('font-size') == '75px'); diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/how-neural-networks-work/deep-learning-demystified.md b/curriculum/challenges/italian/11-machine-learning-with-python/how-neural-networks-work/deep-learning-demystified.md index 7d7338ee65..f08e8a4a82 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/how-neural-networks-work/deep-learning-demystified.md +++ b/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-- diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/how-neural-networks-work/how-convolutional-neural-networks-work.md b/curriculum/challenges/italian/11-machine-learning-with-python/how-neural-networks-work/how-convolutional-neural-networks-work.md index b06b2fbed8..b8ed8546e9 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/how-neural-networks-work/how-convolutional-neural-networks-work.md +++ b/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. --- -If your data is text or sound based. +Se i tuoi dati sono basati su testo o suono. ## --video-solution-- diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/how-neural-networks-work/how-deep-neural-networks-work.md b/curriculum/challenges/italian/11-machine-learning-with-python/how-neural-networks-work/how-deep-neural-networks-work.md index ae41c9be52..e28adce4f6 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/how-neural-networks-work/how-deep-neural-networks-work.md +++ b/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. --- -There is no difference between the two methods. +Non vi è alcuna differenza tra i due metodi. ## --video-solution-- diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/how-neural-networks-work/recurrent-neural-networks-rnn-and-long-short-term-memory-lstm.md b/curriculum/challenges/italian/11-machine-learning-with-python/how-neural-networks-work/recurrent-neural-networks-rnn-and-long-short-term-memory-lstm.md index ab702e9b66..be5b035ea2 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/how-neural-networks-work/recurrent-neural-networks-rnn-and-long-short-term-memory-lstm.md +++ b/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-- diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/machine-learning-with-python-projects/book-recommendation-engine-using-knn.md b/curriculum/challenges/italian/11-machine-learning-with-python/machine-learning-with-python-projects/book-recommendation-engine-using-knn.md index b61ec6d5d1..f868e01843 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/machine-learning-with-python-projects/book-recommendation-engine-using-knn.md +++ b/curriculum/challenges/italian/11-machine-learning-with-python/machine-learning-with-python-projects/book-recommendation-engine-using-knn.md @@ -1,6 +1,6 @@ --- id: 5e46f8e3ac417301a38fb92f -title: Book Recommendation Engine using KNN +title: Motore per la raccomandazione di libri che utilizza KNN challengeType: 10 forumTopicId: 462378 dashedName: book-recommendation-engine-using-knn @@ -8,19 +8,19 @@ dashedName: book-recommendation-engine-using-knn # --description-- -In this challenge, you will create a book recommendation algorithm using K-Nearest Neighbors. +In questa sfida, creerai un algoritmo di raccomandazione di libri utilizzando K-Nearest Neighbors. -You will use the Book-Crossings dataset. This dataset contains 1.1 million ratings (scale of 1-10) of 270,000 books by 90,000 users. +Userai il dataset di Book-Crossings. Questo set di dati contiene 1,1 milioni di valutazioni (scala 1-10) di 270.000 libri da 90.000 utenti. -You can access [the full project instructions and starter code on Google Colaboratory](https://colab.research.google.com/github/freeCodeCamp/boilerplate-book-recommendation-engine/blob/master/fcc_book_recommendation_knn.ipynb). +Puoi accedere [alla descrizione completa del progetto e al codice iniziale su Google Colaboratory](https://colab.research.google.com/github/freeCodeCamp/boilerplate-book-recommendation-engine/blob/master/fcc_book_recommendation_knn.ipynb). -After going to that link, create a copy of the notebook either in your own account or locally. Once you complete the project and it passes the test (included at that link), submit your project link below. If you are submitting a Google Colaboratory link, make sure to turn on link sharing for "anyone with the link." +Dopo essere andato a quel collegamento, crea una copia del notebook nel tuo account o localmente. Una volta completato il progetto e superato il test (incluso a quel link), invia il link del progetto qui sotto. Se stai inviando un link di Google Colaboratory, assicurati di attivare la condivisione di link per "anyone with the link" -We are still developing the interactive instructional content for the machine learning curriculum. For now, you can go through the video challenges in this certification. You may also have to seek out additional learning resources, similar to what you would do when working on a real-world project. +Stiamo ancora sviluppando il contenuto didattico interattivo per il programma di machine learning. Per ora, puoi vedere le sfide video in questa certificazione. Potrebbe anche essere necessario cercare ulteriori risorse di apprendimento, in maniera simile a quello che faresti lavorando su un progetto del mondo reale. # --hints-- -It should pass all Python tests. +Dovrebbe superare tutti i test Python. ```js diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/machine-learning-with-python-projects/cat-and-dog-image-classifier.md b/curriculum/challenges/italian/11-machine-learning-with-python/machine-learning-with-python-projects/cat-and-dog-image-classifier.md index 07288906ad..4a069e127f 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/machine-learning-with-python-projects/cat-and-dog-image-classifier.md +++ b/curriculum/challenges/italian/11-machine-learning-with-python/machine-learning-with-python-projects/cat-and-dog-image-classifier.md @@ -1,6 +1,6 @@ --- id: 5e46f8dcac417301a38fb92e -title: Cat and Dog Image Classifier +title: Classificatore di immagini di gatti e cani challengeType: 10 forumTopicId: 462377 dashedName: cat-and-dog-image-classifier @@ -8,17 +8,17 @@ dashedName: cat-and-dog-image-classifier # --description-- -For this challenge, you will use TensorFlow 2.0 and Keras to create a convolutional neural network that correctly classifies images of cats and dogs with at least 63% accuracy. +Per questa sfida, userai TensorFlow 2.0 e Keras per creare una rete neurale convoluzionale che classifica correttamente le immagini di cani e gatti con almeno il 63% di accuratezza. -You can access [the full project instructions and starter code on Google Colaboratory](https://colab.research.google.com/github/freeCodeCamp/boilerplate-cat-and-dog-image-classifier/blob/master/fcc_cat_dog.ipynb). +Puoi accedere [alla descrizione completa del progetto e al codice iniziale su Google Colaboratory](https://colab.research.google.com/github/freeCodeCamp/boilerplate-cat-and-dog-image-classifier/blob/master/fcc_cat_dog.ipynb). -After going to that link, create a copy of the notebook either in your own account or locally. Once you complete the project and it passes the test (included at that link), submit your project link below. If you are submitting a Google Colaboratory link, make sure to turn on link sharing for "anyone with the link." +Dopo essere andato a quel collegamento, crea una copia del notebook o nel tuo account o localmente. Una volta completato il progetto e superato il test (incluso a quel link), invia il link del progetto qui sotto. Se stai inviando un link di Google Colaboratory, assicurati di attivare la condivisione di link per "anyone with the link" -We are still developing the interactive instructional content for the machine learning curriculum. For now, you can go through the video challenges in this certification. You may also have to seek out additional learning resources, similar to what you would do when working on a real-world project. +Stiamo ancora sviluppando il contenuto didattico interattivo per il programma di machine learning. Per ora, puoi vedere le sfide video in questa certificazione. Potrebbe anche essere necessario cercare ulteriori risorse di apprendimento, in maniera simile a quello che faresti lavorando su un progetto del mondo reale. # --hints-- -It should pass all Python tests. +Dovrebbe superare tutti i test Python. ```js diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/machine-learning-with-python-projects/linear-regression-health-costs-calculator.md b/curriculum/challenges/italian/11-machine-learning-with-python/machine-learning-with-python-projects/linear-regression-health-costs-calculator.md index 56b993d65c..dafeaef096 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/machine-learning-with-python-projects/linear-regression-health-costs-calculator.md +++ b/curriculum/challenges/italian/11-machine-learning-with-python/machine-learning-with-python-projects/linear-regression-health-costs-calculator.md @@ -1,6 +1,6 @@ --- id: 5e46f8edac417301a38fb930 -title: Linear Regression Health Costs Calculator +title: Calcolatore di costi per la salute con la Regressione Lineare challengeType: 10 forumTopicId: 462379 dashedName: linear-regression-health-costs-calculator @@ -8,19 +8,19 @@ dashedName: linear-regression-health-costs-calculator # --description-- -In this challenge, you will predict healthcare costs using a regression algorithm. +In questa sfida, cercherai di prevedere i costi sanitari utilizzando un algoritmo di regressione. -You are given a dataset that contains information about different people including their healthcare costs. Use the data to predict healthcare costs based on new data. +Ti viene dato un set di dati contenente informazioni su persone diverse, inclusi i costi sanitari. Utilizza i dati per prevedere i costi sanitari sulla base di nuovi dati. -You can access [the full project instructions and starter code on Google Colaboratory](https://colab.research.google.com/github/freeCodeCamp/boilerplate-linear-regression-health-costs-calculator/blob/master/fcc_predict_health_costs_with_regression.ipynb). +Puoi accedere [alla descrizione completa del progetto e al codice iniziale su Google Colaboratory](https://colab.research.google.com/github/freeCodeCamp/boilerplate-linear-regression-health-costs-calculator/blob/master/fcc_predict_health_costs_with_regression.ipynb). -After going to that link, create a copy of the notebook either in your own account or locally. Once you complete the project and it passes the test (included at that link), submit your project link below. If you are submitting a Google Colaboratory link, make sure to turn on link sharing for "anyone with the link." +Dopo essere andato a quel collegamento, creare una copia del notebook nel tuo account o localmente. Una volta completato il progetto e superato il test (incluso a quel link), invia il link del progetto qui sotto. Se stai inviando un link di Google Colaboratory, assicurati di attivare la condivisione di link per "anyone with the link" -We are still developing the interactive instructional content for the machine learning curriculum. For now, you can go through the video challenges in this certification. You may also have to seek out additional learning resources, similar to what you would do when working on a real-world project. +Stiamo ancora sviluppando il contenuto didattico interattivo per il programma di machine learning. Per ora, puoi vedere le sfide video in questa certificazione. Potrebbe anche essere necessario cercare ulteriori risorse di apprendimento, in maniera simile a quello che faresti lavorando su un progetto del mondo reale. # --hints-- -It should pass all Python tests. +Dovrebbe superare tutti i test Python. ```js diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/machine-learning-with-python-projects/neural-network-sms-text-classifier.md b/curriculum/challenges/italian/11-machine-learning-with-python/machine-learning-with-python-projects/neural-network-sms-text-classifier.md index c5919ba470..308ae60598 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/machine-learning-with-python-projects/neural-network-sms-text-classifier.md +++ b/curriculum/challenges/italian/11-machine-learning-with-python/machine-learning-with-python-projects/neural-network-sms-text-classifier.md @@ -1,6 +1,6 @@ --- id: 5e46f8edac417301a38fb931 -title: Neural Network SMS Text Classifier +title: Classificatore neurale di messaggi SMS challengeType: 10 forumTopicId: 462380 dashedName: neural-network-sms-text-classifier @@ -8,17 +8,17 @@ dashedName: neural-network-sms-text-classifier # --description-- -In this challenge, you need to create a machine learning model that will classify SMS messages as either "ham" or "spam". A "ham" message is a normal message sent by a friend. A "spam" message is an advertisement or a message sent by a company. +In questa sfida, dovrai creare un modello di machine learning che classificherà i messaggi SMS come "ham" o "spam". Un messaggio "ham" è un normale messaggio inviato da un amico. Un messaggio "spam" è un annuncio o un messaggio inviato da un'azienda. -You can access [the full project instructions and starter code on Google Colaboratory](https://colab.research.google.com/github/freeCodeCamp/boilerplate-neural-network-sms-text-classifier/blob/master/fcc_sms_text_classification.ipynb). +Puoi accedere [alla descrizione completa del progetto e al codice iniziale su Google Colaboratory](https://colab.research.google.com/github/freeCodeCamp/boilerplate-neural-network-sms-text-classifier/blob/master/fcc_sms_text_classification.ipynb). -After going to that link, create a copy of the notebook either in your own account or locally. Once you complete the project and it passes the test (included at that link), submit your project link below. If you are submitting a Google Colaboratory link, make sure to turn on link sharing for "anyone with the link." +Dopo essere andato a quel collegamento, crea una copia del notebook nel tuo account o localmente. Una volta completato il progetto e superato il test (incluso a quel link), invia il link del progetto qui sotto. Se stai inviando un link di Google Colaboratory, assicurati di attivare la condivisione di link per "anyone with the link" -We are still developing the interactive instructional content for the machine learning curriculum. For now, you can go through the video challenges in this certification. You may also have to seek out additional learning resources, similar to what you would do when working on a real-world project. +Stiamo ancora sviluppando il contenuto didattico interattivo per il programma di machine learning. Per ora, puoi vedere le sfide video in questa certificazione. Potrebbe anche essere necessario cercare ulteriori risorse di apprendimento, in maniera simile a quello che faresti lavorando su un progetto del mondo reale. # --hints-- -It should pass all Python tests. +Dovrebbe superare tutti i test Python. ```js diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/convolutional-neural-networks-evaluating-the-model.md b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/convolutional-neural-networks-evaluating-the-model.md index 71a466e2b5..fd715d02eb 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/convolutional-neural-networks-evaluating-the-model.md +++ b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/convolutional-neural-networks-evaluating-the-model.md @@ -1,6 +1,6 @@ --- id: 5e8f2f13c4cdbe86b5c72d99 -title: 'Convolutional Neural Networks: Evaluating the Model' +title: 'Reti neurali convoluzionali: Valutare un modello' challengeType: 11 videoId: eCATNvwraXg dashedName: convolutional-neural-networks-evaluating-the-model @@ -10,19 +10,19 @@ dashedName: convolutional-neural-networks-evaluating-the-model ## --text-- -What is **not** a good way to increase the accuracy of a convolutional neural network? +Quale di questi **non** è un buon metodo per aumentare la precisione di una rete neurale convoluzionale? ## --answers-- -Augmenting the data you already have. +Aumentare i dati che hai già. --- -Using a pre-trained model. +Utilizzare un modello pre-addestrato. --- -Using your test data to retrain the model. +Utilizzare i dati di test per riaddestrare il modello. ## --video-solution-- diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/convolutional-neural-networks-picking-a-pretrained-model.md b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/convolutional-neural-networks-picking-a-pretrained-model.md index 9d34c0b25a..840c8600fa 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/convolutional-neural-networks-picking-a-pretrained-model.md +++ b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/convolutional-neural-networks-picking-a-pretrained-model.md @@ -1,6 +1,6 @@ --- id: 5e8f2f13c4cdbe86b5c72d9a -title: 'Convolutional Neural Networks: Picking a Pretrained Model' +title: 'Reti neurali convoluzionali: scegliere un modello preaddestrato' challengeType: 11 videoId: h1XUt1AgIOI dashedName: convolutional-neural-networks-picking-a-pretrained-model @@ -10,7 +10,7 @@ dashedName: convolutional-neural-networks-picking-a-pretrained-model ## --text-- -Fill in the blanks below to use Google's pre-trained MobileNet V2 model as a base for a convolutional neural network: +Compila gli spazi vuoti qui sotto per utilizzare il modello preaddestrato di Google MobileNet V2 come base per una rete neurale convoluzionale: ```py base_model = tf.__A__.applications.__B__(input_shape=(160, 160, 3), diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/convolutional-neural-networks-the-convolutional-layer.md b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/convolutional-neural-networks-the-convolutional-layer.md index 87c0b9ef9b..b1893bf4ec 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/convolutional-neural-networks-the-convolutional-layer.md +++ b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/convolutional-neural-networks-the-convolutional-layer.md @@ -1,6 +1,6 @@ --- id: 5e8f2f13c4cdbe86b5c72d97 -title: 'Convolutional Neural Networks: The Convolutional Layer' +title: 'Reti neurali convolutionali: Il livello convoluzionale' challengeType: 11 videoId: LrdmcQpTyLw dashedName: convolutional-neural-networks-the-convolutional-layer @@ -10,19 +10,19 @@ dashedName: convolutional-neural-networks-the-convolutional-layer ## --text-- -What are the three main properties of each convolutional layer? +Quali sono le tre proprietà principali di ogni strato convoluzionale? ## --answers-- -Input size, the number of filters, and the sample size of the filters. +Dimensione dell'input, numero di filtri e dimensione del campione dei filtri. --- -Input size, input dimensions, and the color values of the input. +Dimensione dell'input, dimensioni in ingresso e valori di colore dell'input. --- -Input size, input padding, and stride. +Dimensione dell'input, padding in ingresso e passo (stride). ## --video-solution-- diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/convolutional-neural-networks.md b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/convolutional-neural-networks.md index 616ef13476..936fe8767a 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/convolutional-neural-networks.md +++ b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/convolutional-neural-networks.md @@ -1,6 +1,6 @@ --- id: 5e8f2f13c4cdbe86b5c72d96 -title: Convolutional Neural Networks +title: Reti neurali convoluzionali challengeType: 11 videoId: _1kTP7uoU9E dashedName: convolutional-neural-networks @@ -10,19 +10,19 @@ dashedName: convolutional-neural-networks ## --text-- -Dense neural networks analyze input on a global scale and recognize patterns in specific areas. Convolutional neural networks...: +Le reti neurali dense analizzano gli input su scala globale e riconoscono schemi in aree specifiche. Le reti neurali convoluzionali...: ## --answers-- -also analyze input globally and extract features from specific areas. +analizzano anche gli input a livello globale ed estraggono le caratteristiche da aree specifiche. --- -do not work well for image classification or object detection. +non funzionano bene per la classificazione delle immagini o per il rilevamento degli oggetti. --- -scan through the entire input a little at a time and learn local patterns. +scansionano l'intero input un po' alla volta e imparano modelli locali. ## --video-solution-- diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/core-learning-algorithms-building-the-model.md b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/core-learning-algorithms-building-the-model.md index 4b792df4cd..b19c74b0ab 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/core-learning-algorithms-building-the-model.md +++ b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/core-learning-algorithms-building-the-model.md @@ -1,6 +1,6 @@ --- id: 5e8f2f13c4cdbe86b5c72d8e -title: 'Core Learning Algorithms: Building the Model' +title: 'Algoritmi di apprendimento principali: Costruire il modello' challengeType: 11 videoId: 5wHw8BTd2ZQ dashedName: core-learning-algorithms-building-the-model @@ -10,7 +10,7 @@ dashedName: core-learning-algorithms-building-the-model ## --text-- -What kind of estimator/model does TensorFlow recommend using for classification? +Che tipo di stimatore/modello TensorFlow consiglia di utilizzare per la classificazione? ## --answers-- diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/core-learning-algorithms-classification.md b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/core-learning-algorithms-classification.md index 245cf7febe..677fbc9d66 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/core-learning-algorithms-classification.md +++ b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/core-learning-algorithms-classification.md @@ -1,6 +1,6 @@ --- id: 5e8f2f13c4cdbe86b5c72d8d -title: 'Core Learning Algorithms: Classification' +title: 'Algoritmi di apprendimento principali: Classificazione' challengeType: 11 videoId: qFF7ZQNvK9E dashedName: core-learning-algorithms-classification @@ -10,19 +10,19 @@ dashedName: core-learning-algorithms-classification ## --text-- -What is classification? +Che cos'è la classificazione? ## --answers-- -The process of separating data points into different classes. +Il processo di separazione dei punti dati in classi diverse. --- -Predicting a numeric value or forecast based on independent and dependent variables. +Predire un valore numerico o fare una previsione basandoci su variabili indipendenti e dipendenti. --- -None of the above. +Niente di quanto sopra. ## --video-solution-- diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/core-learning-algorithms-clustering.md b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/core-learning-algorithms-clustering.md index 0f8065dedc..2ca5b1f704 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/core-learning-algorithms-clustering.md +++ b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/core-learning-algorithms-clustering.md @@ -1,6 +1,6 @@ --- id: 5e8f2f13c4cdbe86b5c72d8f -title: 'Core Learning Algorithms: Clustering' +title: 'Algoritmi di apprendimento principali: Clustering' challengeType: 11 videoId: 8sqIaHc9Cz4 dashedName: core-learning-algorithms-clustering @@ -10,27 +10,27 @@ dashedName: core-learning-algorithms-clustering ## --text-- -Which of the following steps is **not** part of the K-Means algorithm? +Quale dei seguenti passi **non** fa parte dell'algoritmo K-Means? ## --answers-- -Randomly pick K points to place K centeroids. +Scegliere a caso i punti K per posizionare i centeroidi K. --- -Assign each K point to the closest K centeroid. +Assegnare ogni punto K al centeroide K più vicino. --- -Move each K centeroid into the middle of all of their data points. +Spostare ogni centeroide K nel centro di tutti i suoi punti dati. --- -Shuffle the K points so they're redistributed randomly. +Mischiare i punti K in modo che siano ridistribuiti in modo casuale. --- -Reassign each K point to the closest K centeroid. +Riassegnare ogni punto K al centeroide K più vicino. ## --video-solution-- diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/core-learning-algorithms-hidden-markov-models.md b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/core-learning-algorithms-hidden-markov-models.md index b829085f33..5079939647 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/core-learning-algorithms-hidden-markov-models.md +++ b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/core-learning-algorithms-hidden-markov-models.md @@ -1,6 +1,6 @@ --- id: 5e8f2f13c4cdbe86b5c72d90 -title: 'Core Learning Algorithms: Hidden Markov Models' +title: 'Algoritmi di apprendimento principali: Modelli di Markov a stati nascosti' challengeType: 11 videoId: IZg24y4wEPY dashedName: core-learning-algorithms-hidden-markov-models @@ -10,19 +10,19 @@ dashedName: core-learning-algorithms-hidden-markov-models ## --text-- -What makes a Hidden Markov model different than linear regression or classification? +Che cosa rende un modello di Markov a stati nascosti diverso dalla regressione lineare o dalla classificazione? ## --answers-- -It uses probability distributions to predict future events or states. +Utilizza distribuzioni di probabilità per prevedere eventi o stati futuri. --- -It analyzes the relationship between independent and dependent variables to make predictions. +Analizza il rapporto tra variabili indipendenti e variabili dipendenti per fare previsioni. --- -It separates data points into separate categories. +Separa i punti dati in categorie separate. ## --video-solution-- diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/core-learning-algorithms-the-training-process.md b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/core-learning-algorithms-the-training-process.md index 29ca4b4896..653006bf2f 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/core-learning-algorithms-the-training-process.md +++ b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/core-learning-algorithms-the-training-process.md @@ -1,6 +1,6 @@ --- id: 5e8f2f13c4cdbe86b5c72d8c -title: 'Core Learning Algorithms: The Training Process' +title: 'Algoritmi di apprendimento principali: Il processo di training' challengeType: 11 videoId: _cEwvqVoBhI dashedName: core-learning-algorithms-the-training-process @@ -10,19 +10,19 @@ dashedName: core-learning-algorithms-the-training-process ## --text-- -What are epochs? +Cosa sono le epoche? ## --answers-- -The number of times the model will see the same data. +Il numero di volte che il modello vedrà gli stessi dati. --- -A type of graph. +Un tipo di grafico. --- -The number of elements you feed to the model at once. +Il numero di elementi che fornisci al modello contemporaneamente. ## --video-solution-- diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/core-learning-algorithms-training-and-testing-data.md b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/core-learning-algorithms-training-and-testing-data.md index d31d7425eb..46043410e9 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/core-learning-algorithms-training-and-testing-data.md +++ b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/core-learning-algorithms-training-and-testing-data.md @@ -1,6 +1,6 @@ --- id: 5e8f2f13c4cdbe86b5c72d8b -title: 'Core Learning Algorithms: Training and Testing Data' +title: 'Algoritmi di apprendimento principali: training e test di dati' challengeType: 11 videoId: wz9J1slsi7I dashedName: core-learning-algorithms-training-and-testing-data @@ -10,19 +10,19 @@ dashedName: core-learning-algorithms-training-and-testing-data ## --text-- -What is categorical data? +Che cosa sono i dati categorici? ## --answers-- -Another term for one-hot encoding. +Un altro termine per la codifica a caldo. --- -Any data that is not numeric. +Qualsiasi dato non numerico. --- -Any data that is represented numerically. +Qualsiasi dato rappresentato numericamente. ## --video-solution-- diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/core-learning-algorithms-using-probabilities-to-make-predictions.md b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/core-learning-algorithms-using-probabilities-to-make-predictions.md index 2f77b3c6ea..40cb3ca910 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/core-learning-algorithms-using-probabilities-to-make-predictions.md +++ b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/core-learning-algorithms-using-probabilities-to-make-predictions.md @@ -1,6 +1,6 @@ --- id: 5e8f2f13c4cdbe86b5c72d91 -title: 'Core Learning Algorithms: Using Probabilities to make Predictions' +title: 'Algoritmi di apprendimento principali: Usare le probabilità per fare predizioni' challengeType: 11 videoId: fYAYvLUawnc dashedName: core-learning-algorithms-using-probabilities-to-make-predictions @@ -10,7 +10,7 @@ dashedName: core-learning-algorithms-using-probabilities-to-make-predictions ## --text-- -What TensorFlow module should you import to implement `.HiddenMarkovModel()`? +Quale modulo TensorFlow dovresti importare per implementare `.HiddenMarkovModel()`? ## --answers-- diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/core-learning-algorithms-working-with-data.md b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/core-learning-algorithms-working-with-data.md index bd2c0c44f1..428ac75260 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/core-learning-algorithms-working-with-data.md +++ b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/core-learning-algorithms-working-with-data.md @@ -1,6 +1,6 @@ --- id: 5e8f2f13c4cdbe86b5c72d8a -title: 'Core Learning Algorithms: Working with Data' +title: 'Algoritmi di apprendimento principali: Lavorare con i dati' challengeType: 11 videoId: u85IOSsJsPI dashedName: core-learning-algorithms-working-with-data @@ -10,19 +10,19 @@ dashedName: core-learning-algorithms-working-with-data ## --text-- -What does the pandas `.head()` function do? +Cosa fa la funzione pandas `.head()`? ## --answers-- -Returns the number of entries in a data frame. +Restituisce il numero di voci in un frame di dati. --- -Returns the number of columns in a data frame. +Restituisce il numero di colonne in un frame di dati. --- -By default, shows the first five rows or entries in a data frame. +Per impostazione predefinita, mostra le prime cinque righe o voci in un frame di dati. ## --video-solution-- diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/core-learning-algorithms.md b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/core-learning-algorithms.md index 470a5db663..0a1558d2f8 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/core-learning-algorithms.md +++ b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/core-learning-algorithms.md @@ -1,6 +1,6 @@ --- id: 5e8f2f13c4cdbe86b5c72d89 -title: Core Learning Algorithms +title: Algoritmi di apprendimento principali challengeType: 11 videoId: u5lZURgcWnU dashedName: core-learning-algorithms @@ -10,25 +10,25 @@ dashedName: core-learning-algorithms ## --text-- -Which type of analysis would be best suited for the following problem?: +Quale tipo di analisi sarebbe più adatto per il seguente problema? -You have the average temperature in the month of March for the last 100 years. Using this data, you want to predict the average temperature in the month of March 5 years from now. +Hai la temperatura media nel mese di marzo per gli ultimi 100 anni. Utilizzando questi dati, vuoi prevedere la temperatura media nel mese di marzo a 5 anni da adesso. ## --answers-- -Multiple regression +Regressione multipla --- -Correlation +Correlazione --- -Decision tree +Albero decisionale --- -Linear regression +Regressione lineare ## --video-solution-- diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/creating-a-convolutional-neural-network.md b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/creating-a-convolutional-neural-network.md index 68999197b2..9a609939d0 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/creating-a-convolutional-neural-network.md +++ b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/creating-a-convolutional-neural-network.md @@ -1,6 +1,6 @@ --- id: 5e8f2f13c4cdbe86b5c72d98 -title: Creating a Convolutional Neural Network +title: Creare una rete neurale convoluzionale challengeType: 11 videoId: kfv0K8MtkIc dashedName: creating-a-convolutional-neural-network @@ -10,7 +10,7 @@ dashedName: creating-a-convolutional-neural-network ## --text-- -Fill in the blanks below to complete the architecture for a convolutional neural network: +Riempi gli spazi vuoti qui sotto per completare l'architettura di una rete neurale convoluzionale: ```py model = models.__A__() diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/introduction-machine-learning-fundamentals.md b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/introduction-machine-learning-fundamentals.md index 185278bb1e..e66c073da8 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/introduction-machine-learning-fundamentals.md +++ b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/introduction-machine-learning-fundamentals.md @@ -1,6 +1,6 @@ --- id: 5e8f2f13c4cdbe86b5c72d87 -title: 'Introduction: Machine Learning Fundamentals' +title: 'Introduzione: Fondamenti di machine learning' challengeType: 11 videoId: KwL1qTR5MT8 dashedName: introduction-machine-learning-fundamentals @@ -10,19 +10,19 @@ dashedName: introduction-machine-learning-fundamentals ## --text-- -Which statement below is **false**? +Quale delle dichiarazioni qui sotto è **falsa**? ## --answers-- -Neural networks are modeled after the way the human brain works. +Le reti neurali sono modellate secondo il modo in cui funziona il cervello umano. --- -Computer programs that play tic-tac-toe or chess against human players are examples of simple artificial intelligence. +Programmi informatici che giocano a tic-tac-toe o scacchi contro i giocatori umani sono esempi di semplice intelligenza artificiale. --- -Machine learning is a subset of artificial intelligence. +L'apprendimento automatico è un sottoinsieme dell'intelligenza artificiale. ## --video-solution-- diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/introduction-to-tensorflow.md b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/introduction-to-tensorflow.md index cefca2bfe2..803f032855 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/introduction-to-tensorflow.md +++ b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/introduction-to-tensorflow.md @@ -1,6 +1,6 @@ --- id: 5e8f2f13c4cdbe86b5c72d88 -title: Introduction to TensorFlow +title: Introduzione a TensorFlow challengeType: 11 videoId: r9hRyGGjOgQ dashedName: introduction-to-tensorflow @@ -10,11 +10,11 @@ dashedName: introduction-to-tensorflow ## --text-- -Which of the following is **not** a type of tensor? +Quale dei seguenti elementi **non** è un tipo di tensore? ## --answers-- -Variable +Variabile --- @@ -22,7 +22,7 @@ Flowing --- -Placeholder +Segnaposto (placeholder) --- @@ -30,7 +30,7 @@ SparseTensor --- -Constant +Costante ## --video-solution-- diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-building-the-model.md b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-building-the-model.md index 7b7546ba33..4e78fac577 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-building-the-model.md +++ b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-building-the-model.md @@ -1,6 +1,6 @@ --- id: 5e8f2f13c4cdbe86b5c72da1 -title: 'Natural Language Processing With RNNs: Building the Model' +title: 'Elaborazione del linguaggio naturale con RNN: costruzione del modello' challengeType: 11 videoId: 32WBFS7lfsw dashedName: natural-language-processing-with-rnns-building-the-model @@ -10,7 +10,7 @@ dashedName: natural-language-processing-with-rnns-building-the-model ## --text-- -Fill in the blanks below to complete the `build_model` function: +Riempi gli spazi vuoti per completare la funzione `build_model` qui sotto: ```py def build_mode(vocab_size, embedding_dim, rnn_units, batch_size): diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-create-a-play-generator.md b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-create-a-play-generator.md index f3a22f813d..d395844eed 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-create-a-play-generator.md +++ b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-create-a-play-generator.md @@ -1,6 +1,6 @@ --- id: 5e8f2f13c4cdbe86b5c72da0 -title: 'Natural Language Processing With RNNs: Create a Play Generator' +title: 'Elaborazione del linguaggio naturale con RNN: Crea un generatore di gioco' challengeType: 11 videoId: j5xsxjq_Xk8 dashedName: natural-language-processing-with-rnns-create-a-play-generator @@ -10,7 +10,7 @@ dashedName: natural-language-processing-with-rnns-create-a-play-generator ## --text-- -Fill in the blanks below to create the training examples for the RNN: +Compila gli spazi vuoti qui sotto per creare il modello per la RNN: ```py char_dataset = tf.data.__A__.__B__(text_as_int) diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-making-predictions.md b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-making-predictions.md index 29dbc05f8d..77ba3fade3 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-making-predictions.md +++ b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-making-predictions.md @@ -1,6 +1,6 @@ --- id: 5e8f2f13c4cdbe86b5c72d9f -title: 'Natural Language Processing With RNNs: Making Predictions' +title: 'Elaborazione del linguaggio naturale con RNN: fare previsioni' challengeType: 11 videoId: WO1hINnBj20 dashedName: natural-language-processing-with-rnns-making-predictions @@ -10,19 +10,19 @@ dashedName: natural-language-processing-with-rnns-making-predictions ## --text-- -Before you make a prediction with your own review, you should...: +Prima di fare una previsione con la propria revisione, si dovrebbe...: ## --answers-- -decode the training dataset and compare the results to the test data. +decodificare il set di dati del training e confrontare i risultati con i dati del test. --- -use the encodings from the training dataset to encode your review. +usa le codifiche del set di dati del training per codificare la tua revisione. --- -assign random values between 0 and the maximum number of vocabulary in your dataset to each word in your review. +assegna valori casuali tra 0 e il numero massimo di vocabolari nel tuo set di dati a ogni parola nella tua revisione. ## --video-solution-- diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-part-2.md b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-part-2.md index c572d154e1..20ca38da4f 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-part-2.md +++ b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-part-2.md @@ -1,6 +1,6 @@ --- id: 5e8f2f13c4cdbe86b5c72d9c -title: 'Natural Language Processing With RNNs: Part 2' +title: 'Elaborazione del linguaggio naturale con RNN: Parte 2' challengeType: 11 videoId: mUU9YXOFbZg dashedName: natural-language-processing-with-rnns-part-2 @@ -10,19 +10,19 @@ dashedName: natural-language-processing-with-rnns-part-2 ## --text-- -Word embeddings are...: +Gli incorporamenti di parole sono...: ## --answers-- -an unordered group of encoded words that describes the frequency of words in a given document. +un gruppo non ordinato di parole codificate che descrive la frequenza delle parole in un dato documento. --- -a group of encoded words that preserves the original order of the words in a given document. +un gruppo di parole codificate che preserva l'ordine originale delle parole in un dato documento. --- -a vectorized representation of words in a given document that places words with similar meanings near each other. +una rappresentazione vettoriale delle parole in un dato documento che mette le parole con significati simili vicino l'una all'altra. ## --video-solution-- diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-recurring-neural-networks.md b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-recurring-neural-networks.md index e3d8d7e263..ffdab99a93 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-recurring-neural-networks.md +++ b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-recurring-neural-networks.md @@ -1,6 +1,6 @@ --- id: 5e8f2f13c4cdbe86b5c72d9d -title: 'Natural Language Processing With RNNs: Recurring Neural Networks' +title: 'Elaborazione del linguaggio naturale con RNN: Reti Neurali Ricorrenti' challengeType: 11 videoId: bX5681NPOcA dashedName: natural-language-processing-with-rnns-recurring-neural-networks @@ -10,23 +10,23 @@ dashedName: natural-language-processing-with-rnns-recurring-neural-networks ## --text-- -What is true about Recurrent Neural Networks? +Quale di queste opzioni è vera per le reti neurali ricorrenti? ## --answers-- -1: They are a type of feed-forward neural network. +1: Sono un tipo di rete neurale feed-forward. --- -2: They maintain an internal memory/state of the input that was already processed. +2: Mantengono una memoria/stato interno dell'input già elaborato. --- -3: RNN's contain a loop and process one piece of input at a time. +3: Le RNN contengono un ciclo ed elaborano un pezzo di ingresso alla volta. --- -4: Both 2 and 3. +4: Sia 2 che 3. ## --video-solution-- diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-sentimental-analysis.md b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-sentimental-analysis.md index 60118b25c8..e3a9eb740c 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-sentimental-analysis.md +++ b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-sentimental-analysis.md @@ -1,6 +1,6 @@ --- id: 5e8f2f13c4cdbe86b5c72d9e -title: 'Natural Language Processing With RNNs: Sentiment Analysis' +title: 'Elaborazione del linguaggio naturale con RNN: analisi del sentimento' challengeType: 11 videoId: lYeLtu8Nq7c dashedName: natural-language-processing-with-rnns-sentiment-analysis @@ -10,7 +10,7 @@ dashedName: natural-language-processing-with-rnns-sentiment-analysis ## --text-- -Fill in the blanks below to create the model for the RNN: +Compila gli spazi vuoti qui sotto per creare il modello per la RNN: ```py model = __A__.keras.Sequential([ diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-training-the-model.md b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-training-the-model.md index 2ae0cab5b7..2c09f9d7f6 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-training-the-model.md +++ b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns-training-the-model.md @@ -1,6 +1,6 @@ --- id: 5e8f2f13c4cdbe86b5c72da2 -title: 'Natural Language Processing With RNNs: Training the Model' +title: 'Elaborazione del linguaggio naturale con RNN: training del modello' challengeType: 11 videoId: hEUiK7j9UI8 dashedName: natural-language-processing-with-rnns-training-the-model @@ -10,7 +10,7 @@ dashedName: natural-language-processing-with-rnns-training-the-model ## --text-- -Fill in the blanks below to save your model's checkpoints in the `./checkpoints` directory and call the latest checkpoint for training: +Compila gli spazi vuoti qui sotto per salvare i punti di controllo del tuo modello nella directory `./checkpoints` e chiamare l'ultimo checkpoint per il training: ```py checkpoint_dir = __A__ diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns.md b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns.md index f4bde45483..5a0f8f1982 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns.md +++ b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/natural-language-processing-with-rnns.md @@ -1,6 +1,6 @@ --- id: 5e8f2f13c4cdbe86b5c72d9b -title: Natural Language Processing With RNNs +title: Elaborazione del linguaggio naturale con le RNN challengeType: 11 videoId: ZyCaF5S-lKg dashedName: natural-language-processing-with-rnns @@ -10,19 +10,19 @@ dashedName: natural-language-processing-with-rnns ## --text-- -Natural Language Processing is a branch of artificial intelligence that...: +L'elaborazione del linguaggio naturale è un ramo dell'intelligenza artificiale che...: ## --answers-- -deals with how computers understand and process natural/human languages. +si occupa di come i computer capiscono ed elaborano le lingue naturali/umane. --- -translates image data into natural/human languages. +traduce i dati delle immagini in lingue naturali/umane. --- -is focused on translating computer languages into natural/human languages. +si concentra sulla traduzione delle lingue informatiche in lingue naturali/umane. ## --video-solution-- diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/neural-networks-activation-functions.md b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/neural-networks-activation-functions.md index 635e29a35d..fb67821e5f 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/neural-networks-activation-functions.md +++ b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/neural-networks-activation-functions.md @@ -1,6 +1,6 @@ --- id: 5e8f2f13c4cdbe86b5c72d93 -title: 'Neural Networks: Activation Functions' +title: 'Reti Neurali: funzioni di attivazione' challengeType: 11 videoId: S45tqW6BqRs dashedName: neural-networks-activation-functions @@ -10,15 +10,15 @@ dashedName: neural-networks-activation-functions ## --text-- -Which activation function switches values between -1 and 1? +Quale funzione di attivazione passa i valori da -1 a 1? ## --answers-- -ReLU (Rectified Linear Unit) +ReLU (Unità Lineare Rettificata) --- -Tanh (Hyperbolic Tangent) +Tanh (Tangente Iperbolica) --- diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/neural-networks-creating-a-model.md b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/neural-networks-creating-a-model.md index 05b3df57e0..6f0dd4bcb6 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/neural-networks-creating-a-model.md +++ b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/neural-networks-creating-a-model.md @@ -1,6 +1,6 @@ --- id: 5e8f2f13c4cdbe86b5c72d95 -title: 'Neural Networks: Creating a Model' +title: 'Reti neurali: creare un modello' challengeType: 11 videoId: K8bz1bmOCTw dashedName: neural-networks-creating-a-model @@ -10,7 +10,7 @@ dashedName: neural-networks-creating-a-model ## --text-- -Fill in the blanks below to build a sequential model of dense layers: +Riempi gli spazi vuoti qui sotto per costruire un modello sequenziale di livelli densi: ```py model = __A__.__B__([ diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/neural-networks-optimizers.md b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/neural-networks-optimizers.md index 216b851ab5..85ac8694a4 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/neural-networks-optimizers.md +++ b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/neural-networks-optimizers.md @@ -1,6 +1,6 @@ --- id: 5e8f2f13c4cdbe86b5c72d94 -title: 'Neural Networks: Optimizers' +title: 'Reti Neurali: Ottimizzatori' challengeType: 11 videoId: hdOtRPQe1o4 dashedName: neural-networks-optimizers @@ -10,19 +10,19 @@ dashedName: neural-networks-optimizers ## --text-- -What is an optimizer function? +Cos'è una funzione di ottimizzazione? ## --answers-- -A function that increases the accuracy of a model's predictions. +Una funzione che aumenta l'accuratezza delle previsioni di un modello. --- -A function that implements the gradient descent and backpropagation algorithms for you. +Una funzione che implementa gli algoritmi di discesa e retropropagazione del gradiente per te. --- -A function that reduces the time a model needs to train. +Una funzione che riduce il tempo necessario per addestrare un modello. ## --video-solution-- diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/neural-networks-with-tensorflow.md b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/neural-networks-with-tensorflow.md index d9d07b00fc..8623b61b42 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/neural-networks-with-tensorflow.md +++ b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/neural-networks-with-tensorflow.md @@ -1,6 +1,6 @@ --- id: 5e8f2f13c4cdbe86b5c72d92 -title: Neural Networks with TensorFlow +title: Reti neurali con TensorFlow challengeType: 11 videoId: uisdfrNrZW4 dashedName: neural-networks-with-tensorflow @@ -10,19 +10,19 @@ dashedName: neural-networks-with-tensorflow ## --text-- -A densely connected neural network is one in which...: +Una rete neurale densamente collegata è una in cui...: ## --answers-- -all the neurons in the current layer are connected to one neuron in the previous layer. +tutti i neuroni nello strato corrente sono collegati a un neurone nello strato precedente. --- -all the neurons in each layer are connected randomly. +tutti i neuroni in ogni livello sono collegati in modo casuale. --- -all the neurons in the current layer are connected to every neuron in the previous layer. +tutti i neuroni dello strato corrente sono collegati ad ogni neurone dello strato precedente. ## --video-solution-- diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/reinforcement-learning-with-q-learning-example.md b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/reinforcement-learning-with-q-learning-example.md index 1b9f6923a9..07adc6676a 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/reinforcement-learning-with-q-learning-example.md +++ b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/reinforcement-learning-with-q-learning-example.md @@ -1,6 +1,6 @@ --- id: 5e8f2f13c4cdbe86b5c72da5 -title: 'Reinforcement Learning With Q-Learning: Example' +title: 'Apprendimento per rinforzo con Q-Learning: Esempio' challengeType: 11 videoId: RBBSNta234s dashedName: reinforcement-learning-with-q-learning-example @@ -10,7 +10,7 @@ dashedName: reinforcement-learning-with-q-learning-example ## --text-- -Fill in the blanks to complete the following Q-Learning equation: +Compila gli spazi vuoti per completare la seguente equazione di Q-Learning: ```py Q[__A__, __B__] = Q[__A__, __B__] + LEARNING_RATE * (reward + GAMMA * np.max(Q[__C__, :]) - Q[__A__, __B__]) diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/reinforcement-learning-with-q-learning-part-2.md b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/reinforcement-learning-with-q-learning-part-2.md index 089f144038..adc709db72 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/reinforcement-learning-with-q-learning-part-2.md +++ b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/reinforcement-learning-with-q-learning-part-2.md @@ -1,6 +1,6 @@ --- id: 5e8f2f13c4cdbe86b5c72da4 -title: 'Reinforcement Learning With Q-Learning: Part 2' +title: 'Apprendimento per rinforzo con Q-Learning: Parte 2' challengeType: 11 videoId: DX7hJuaUZ7o dashedName: reinforcement-learning-with-q-learning-part-2 @@ -10,15 +10,15 @@ dashedName: reinforcement-learning-with-q-learning-part-2 ## --text-- -What can happen if the agent does not have a good balance of taking random actions and using learned actions? +Cosa può accadere se l'agente non ha un buon equilibrio tra l'intraprendere azioni casuali e l'utilizzare azioni apprese? ## --answers-- -The agent will always try to minimize its reward for the current state/action, leading to local minima. +L'agente cercherà sempre di minimizzare la sua ricompensa per lo stato o l'azione corrente, portando a minimi locali. --- -The agent will always try to maximize its reward for the current state/action, leading to local maxima. +L'agente cercherà sempre di massimizzare la sua ricompensa per lo stato o l'azione corrente, portando a massimi locali. ## --video-solution-- diff --git a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/reinforcement-learning-with-q-learning.md b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/reinforcement-learning-with-q-learning.md index 3cbeedcfb3..8918f9d324 100644 --- a/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/reinforcement-learning-with-q-learning.md +++ b/curriculum/challenges/italian/11-machine-learning-with-python/tensorflow/reinforcement-learning-with-q-learning.md @@ -1,6 +1,6 @@ --- id: 5e8f2f13c4cdbe86b5c72da3 -title: Reinforcement Learning With Q-Learning +title: Apprendimento per rinforzo con Q-Learning challengeType: 11 videoId: Cf7DSU0gVb4 dashedName: reinforcement-learning-with-q-learning @@ -10,19 +10,19 @@ dashedName: reinforcement-learning-with-q-learning ## --text-- -The key components of reinforcement learning are... +Le componenti chiave dell'apprendimento per rinforzo sono... ## --answers-- -environment, representative, state, reaction, and reward. +ambiente, rappresentazione, stato, reazione e ricompensa. --- -environment, agent, state, action, and reward. +ambiente, agente, stato, azione e ricompensa. --- -habitat, agent, state, action, and punishment. +habitat, agente, stato, azione e punizione. ## --video-solution-- diff --git a/curriculum/challenges/portuguese/05-apis-and-microservices/basic-node-and-express/get-query-parameter-input-from-the-client.md b/curriculum/challenges/portuguese/05-apis-and-microservices/basic-node-and-express/get-query-parameter-input-from-the-client.md index 0d301a9f5e..f86416c71f 100644 --- a/curriculum/challenges/portuguese/05-apis-and-microservices/basic-node-and-express/get-query-parameter-input-from-the-client.md +++ b/curriculum/challenges/portuguese/05-apis-and-microservices/basic-node-and-express/get-query-parameter-input-from-the-client.md @@ -1,6 +1,6 @@ --- id: 587d7fb2367417b2b2512bf6 -title: Get Query Parameter Input from the Client +title: Obter a entrada do parâmetro da consulta do cliente challengeType: 2 forumTopicId: 301512 dashedName: get-query-parameter-input-from-the-client @@ -8,19 +8,19 @@ dashedName: get-query-parameter-input-from-the-client # --description-- -Another common way to get input from the client is by encoding the data after the route path, using a query string. The query string is delimited by a question mark (?), and includes field=value couples. Each couple is separated by an ampersand (&). Express can parse the data from the query string, and populate the object `req.query`. Some characters, like the percent (%), cannot be in URLs and have to be encoded in a different format before you can send them. If you use the API from JavaScript, you can use specific methods to encode/decode these characters. +Outro jeito usual de obter a entrada do cliente é ao codificar os dados após o caminho da rota, usando uma string de consulta. A string de consulta é delimitada por um símbolo de interrogação (?) e inclui pares campo=valor. Cada par é separado por um e comercial(&). O Express pode analisar os dados da string de consulta e preencher o objeto `req.query`. Alguns caracteres, como o de porcentagem (%), não podem estar nos URLs e tem de ser codificados em um formato diferente antes que você os envie. Se você usa a API do JavaScript, você pode usar métodos específicos para codificar/decodificar esses caracteres.
route_path: '/library'
actual_request_URL: '/library?userId=546&bookId=6754'
req.query: {userId: '546', bookId: '6754'}
# --instructions-- -Build an API endpoint, mounted at `GET /name`. Respond with a JSON document, taking the structure `{ name: 'firstname lastname'}`. The first and last name parameters should be encoded in a query string e.g. `?first=firstname&last=lastname`. +Faça uma API de endpoint (URL), montada em `GET /name`. Responda com um documento JSON, pegando a estrutura `{ name: 'firstname lastname'}`. O parâmetros primeiro e último nome devem ser codificados em uma string de consulta como, por exemplo: `?first=firstname&last=lastname`. -**Note:** In the following exercise you are going to receive data from a POST request, at the same `/name` route path. If you want, you can use the method `app.route(path).get(handler).post(handler)`. This syntax allows you to chain different verb handlers on the same path route. You can save a bit of typing, and have cleaner code. +**Nota:** no exercício seguinte, você vai receber dados de uma requisição POST, no mesmo caminho de rota `/name`. Se você quiser, poderá usar o método `app.route(path).get(handler).post(handler)`. Essa sintaxe permite a você encadear diferentes manipuladores do tipo verb no mesmo caminho de rota. Você vai economizar na digitação e ter um código mais limpo. # --hints-- -Test 1 : Your API endpoint should respond with the correct name +Teste 1: O endpoint (URL) da API deverá responder com o nome correto ```js (getUserInput) => @@ -38,7 +38,7 @@ Test 1 : Your API endpoint should respond with the correct name ); ``` -Test 2 : Your API endpoint should respond with the correct name +Teste 2 : O endpoint (URL) da API deve responder com o nome correto ```js (getUserInput) =>