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Joaquim Castilla edited this page Sep 23, 2020
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- Try to pick up all of the files from the same week. Most of the time, EN files from the same week are homogeneous in writers, topics, and style. We wish to keep the structure consistent also in the [YOUR_LANG] files. Especially if the [YOUR_LANG] translations are authored by different people, try to keep the translation of terms, particularly the technical and mathematical ones, consistent throughout 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.
| File name | Translator | Start date | Publish date | Reviewers | PR | Notes |
|---|---|---|---|---|---|---|
README.md |
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index.md |
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01.md |
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01-1.md |
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01-2.md |
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01-3.md |
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lecture01.sbv |
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practicum01.sbv |
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02.md |
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02-1.md |
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02-2.md |
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02-3.md |
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lecture02.sbv |
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practicum02.sbv |
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03.md |
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03-1.md |
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03-2.md |
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03-3.md |
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lecture03.sbv |
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practicum03.sbv |
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04.md |
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04-1.md |
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practicum04.sbv |
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05.md |
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05-1.md |
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05-2.md |
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05-3.md |
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lecture05.sbv |
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practicum05.sbv |
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06.md |
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06-1.md |
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06-2.md |
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06-3.md |
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07.md |
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07-1.md |
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07-2.md |
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07-3.md |
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08.md |
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08-1.md |
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08-2.md |
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08-3.md |
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09.md |
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09-1.md |
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09-2.md |
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09-3.md |
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10.md |
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10-1.md |
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10-2.md |
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10-3.md |
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11.md |
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11-1.md |
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11-2.md |
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11-3.md |
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12.md |
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12-1.md |
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12-2.md |
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12-3.md |
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13.md |
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13-1.md |
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13-2.md |
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13-3.md |
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14.md |
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14-1.md |
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14-2.md |
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14-3.md |
| Term | Translation |
|---|---|
| Class | |
| Lecture | |
| Practicum |
| Term | Translation |
|---|---|
| activation | |
| activation function | |
| adaline | |
| affine transformation | |
| (artificial) neural network | |
| autoencoder | |
| autonomous vehicles | |
| backpropagation | |
| batch normalization | |
| bias | |
| chain rule | |
| computer vision | |
| contrast normalization | |
| convolution | |
| cost function | |
| cybernetic | |
| deep-learning | |
| dropout | |
| embedding | |
| energy-based model | |
| ensemble | |
| feature | |
| fire (of neuron) | |
| fully connected layer | |
| fully connected network | |
| gradient | |
| gradient descent | |
| hidden layer | |
| hierarchical representation | |
| image classification | |
| image segmentation | |
| inference | |
| Jacobian matrix | |
| Jupyter Notebook | |
| label | |
| lane tracking | |
| latent space | |
| layer | |
| layers | |
| lecture part A | |
| logistic regression | |
| loss function | |
| Nash equilibrium | |
| natural language understanding | |
| natural language processing | |
| nearest neighbor | |
| non-maximum suppression | |
| norm | |
| object detection | |
| one-hot | |
| parameter | |
| pattern recognition | |
| perceptron | |
| pooling | |
| practicum | |
| recurrent neural networks | |
| reflection | |
| regularization | |
| rotation | |
| scaling | |
| self-supervised learning | |
| scalar | |
| semantic segmentation | |
| shearing | |
| softmax, soft (arg)max | |
| speech recognition | |
| stochastic gradient descent | |
| supervised learning | |
| tensor | |
| translation | |
| trajectory | |
| unsupervised learning | |
| visual cortex | |
| weight | |
| weighted sum |
| Term | Explanation |
|---|---|
| actor critic | |
| CNN | Convolutional Neural Network |
| GAN | generative adversarial networks |
| GPU | Graphic Processing Unit |
| ReLU | Rectified linear unit |