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Remove duplicate code cell and fix typos (#369)

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* Remove duplicate code cell

* Correct typo

* Corect `ou model` to `our model`
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Rehman Amjad 2021-01-17 18:10:02 +00:00 committed by GitHub
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2 changed files with 2 additions and 71 deletions
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2
12_nlp_dive.ipynb
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@ -2286,7 +2286,7 @@
"source": [
"1. If the dataset for your project is so big and complicated that working with it takes a significant amount of time, what should you do?\n",
"1. Why do we concatenate the documents in our dataset before creating a language model?\n",
"1. To use a standard fully connected network to predict the fourth word given the previous three words, what two tweaks do we need to make to ou model?\n",
"1. To use a standard fully connected network to predict the fourth word given the previous three words, what two tweaks do we need to make to our model?\n",
"1. How can we share a weight matrix across multiple layers in PyTorch?\n",
"1. Write a module that predicts the third word given the previous two words of a sentence, without peeking.\n",
"1. What is a recurrent neural network?\n",

71
13_convolutions.ipynb
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@ -2633,7 +2633,7 @@
"source": [
"We saw what the convolution operation was for one filter on one channel of the image (our examples were done on a square). A convolutional layer will take an image with a certain number of channels (three for the first layer for regular RGB color images) and output an image with a different number of channels. Like our hidden size that represented the numbers of neurons in a linear layer, we can decide to have as many filters as we want, and each of them will be able to specialize, some to detect horizontal edges, others to detect vertical edges and so forth, to give something like we studied in <<chapter_production>>.\n",
"\n",
"In one sliding window, we have a certain number of channels and we need as many filters (we don't use the same kernel for all the channels). So our kernel doesn't have a size of 3 by 3, but `ch_in` (for channels in) by 3 by 3. On each channel, we multiply the elements of our window by the elements of the coresponding filter, then sum the results (as we saw before) and sum over all the filters. In the example given in <<rgbconv>>, the result of our conv layer on that window is red + green + blue."
"In one sliding window, we have a certain number of channels and we need as many filters (we don't use the same kernel for all the channels). So our kernel doesn't have a size of 3 by 3, but `ch_in` (for channels in) is 3 by 3. On each channel, we multiply the elements of our window by the elements of the coresponding filter, then sum the results (as we saw before) and sum over all the filters. In the example given in <<rgbconv>>, the result of our conv layer on that window is red + green + blue."
]
},
{
@ -3577,75 +3577,6 @@
"learn = fit(5, lr=0.1)"
]
},
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" <th>epoch</th>\n",
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" <th>valid_loss</th>\n",
" <th>accuracy</th>\n",
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"source": [
"learn = fit(5, lr=0.1)"
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