Italian translation - Atcold/NYU-DLSP20 GitHub Wiki
Guidelines for reviewers:
- Try to pick up all of the files from the same week. Most of the times, EN files from the same week are homogeneous in writers, topics, and style. We wish to keep the structure consistent also in the IT files. Especially if the IT translations are authored by different people, try to keep the translation of terms, particularly the techical and mathematical ones, consistent throughought the files.
- Try to keep the translation of terms consistent with the #Terms table below. If you're unsure about a translation, ask on Slack, have no fear :)
- After having completed the review, create another Pull Request. Write the PR number in the Notes column of the workload distribution, like so "Reviewed (#000)". When the PR is approved, write Review approved in the same column.
Coordinators
Marco
Workload distribution
The official workload distribution table has been replaced with a Google Sheet accessible only to the coordinators (see above). Please notify the coordinators via Slack when you want to pick up some new work. This table is for reference only.
Please type the dates in the MM/DD/YY format as well.
Terminology conventions
Rules
# Acronimi non si cambiano.
CNN -> CNN
# Aggiungere la parentesi la prima volta che usa il termine nel testo.
Convolutional neural nets -> rete neurale convoluzionale (CNN, Convolutional Neural Network)
# Dropout non ha traduzione, poiché non usato al di fuori di gergo AI/ML
Dropout -> Dropout
# Concetto del codificatore è presente in vari campi ingegneristici, quindi usiamo la parola italiana.
# Aggiungere la parentesi la prima volta che usa il termine nel testo.
Encoder -> Codificatore (Encoder)
Terms
| Term |
Translation |
| activation |
attivazione |
| activation function |
funzione di attivazione |
| affine transformation |
trasformazione affine |
| (artificial) neural network |
rete neurale (artificiale) |
| annealing |
annealing / ricottura |
| autoencoder |
auto-codificatore / autoencoder |
| autonomous vehicles |
veicoli autonomi |
| backpropagation |
retropropagazione |
| batch |
batch |
| batch normalization |
normalizzazione del batch |
| bias |
distorsione / bias |
| channel |
canale |
| chain rule |
regola della catena |
| computer vision |
visione artificiale |
| contrast normalization |
normalizzazione del contrasto |
| convolution |
convoluzione |
| cosine similarity |
coseno di similarità |
| cost function |
funzione di costo / funzione di perdita |
| cross-correlation |
correlazione incrociata |
| cybernetic |
cibernetico |
| deep-learning |
apprendimento profondo |
| detection |
identificazione |
| dropout |
dropout |
| embedding |
embedding |
| ensemble |
ensemble |
| explicit |
esplicito |
| energy based model (EBM) |
modelli ad energia |
| feed-forward neural network |
rete neurale feed-forward |
| fully connected layer |
strato densamente connesso |
| gradient descent |
discesa del gradiente / gradient descent |
| hierarchical representation |
rappresentazione gerarchica |
| [neural] hyper learning |
iper-apprendimento |
| image classification |
classificazione d'immagini |
| image segmentation |
segmentazione d'immagini |
| implicit |
implicito |
| inference |
inferenza |
| Jacobian matrix |
matrice jacobiana |
| Jupyter Notebook |
Jupyter notebook |
| kernel (of a matrix) |
nucleo |
| kernel (of a convolution) |
filtro |
| lane tracking |
tracciamento corsia |
| logistic regression |
regressione logistica |
| loss function |
funzione di perdita |
| machine learning |
apprendimento automatico |
| manifold |
varietà (manifold) |
| Nash equilibrium |
equilibrio di Nash |
| natural language understanding |
comprensione del linguaggio naturale |
| natural language processing |
elaborazione del linguaggio naturale |
| nearest neighbour |
|
| non-maximum suppression |
soppressione dei non-massimi |
| object detection |
|
| one-hot |
|
| overfitting |
sovradattamento |
| overcomplete / undercomplete |
sovracompleto / sottocompleto |
| padding |
|
| pattern recognition |
riconoscimento di pattern |
| pooling |
pooling |
| reflection |
|
| recognition |
riconoscimento |
| regularization |
regolarizzazione |
| rotation |
rotazione |
| scaling |
scalatura |
| self-supervised learning |
apprendimento auto-supervisionato |
| scalar |
scalare |
| semantic segmentation |
|
| shearing |
|
| speech recognition |
riconoscimento vocale |
| stochastic gradient descent |
discesa stocastica del gradiente |
| supervised learning |
apprendimento supervisionato |
| tensor |
tensore |
| translation |
traslazione |
| trajectory |
|
| unsupervised learning |
apprendimento non-supervisionato |
| upsampling |
sovracampionamento |
| visual cortex |
corteccia visiva |
| weight |
peso/parametro |
| weighted sum |
somma pesata |
| cross-entropy loss |
perdita di entropia incrociata |
| Term |
Explanation |
| actor critic |
Actor Critic |
| CNN |
Convolutional Neural Network |
| GAN |
generative adversarial networks |
| GPU |
Graphic Processing Unit |
| ReLU |
Rectified linear unit |