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Merge pull request #76 from alvarotap/patch-8

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Some minor typos.
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Sylvain Gugger 2020-03-31 19:19:45 -04:00 committed by GitHub
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06_multicat.ipynb
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@ -191,7 +191,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"No, it’s not actually a panda! *Pandas* is a Python library that is used to manipulate and analysis tabular and timeseries data. The main class is `DataFrame`, which represents a table of rows and columns. You can get a DataFrame from a CSV file, a database table, python dictionaries, and many other sources. In Jupyter, a DataFrame is output as a formatted table, as you see above.\n",
"No, it’s not actually a panda! *Pandas* is a Python library that is used to manipulate and analyze tabular and timeseries data. The main class is `DataFrame`, which represents a table of rows and columns. You can get a DataFrame from a CSV file, a database table, python dictionaries, and many other sources. In Jupyter, a DataFrame is output as a formatted table, as you see above.\n",
"\n",
"You can access rows and columns of a DataFrame with the `iloc` property, which lets you access rows and columns as if it is a matrix:"
]
@ -1192,7 +1192,7 @@
"source": [
"In this case, we're using the validation set to pick a hyperparameter (the threshold), which is the purpose of the validation set. But sometimes students have expressed their concern that we might be *overfitting* to the validation set, since we're trying lots of values to see which is the best. However, as you see in the plot, changing the threshold in this case results in a smooth curve, so we're clearly not picking some inappropriate outlier. This is a good example of where you have to be careful of the difference between theory (don't try lots of hyperparameter values or you might overfit the validation set) versus practice (if the relationship is smooth, then it's fine to do this).\n",
"\n",
"This concludes the part of thic chapter dedicated to multi-label classification. Let's have a look at a regression problem now."
"This concludes the part of this chapter dedicated to multi-label classification. Let's have a look at a regression problem now."
]
},
{
@ -1459,7 +1459,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"> important: We're not aware of other libraries (except for fastai) that automatically and correctly apply data augmentation to coordinates. So if you're working with another library, you may need to disable data augmentation for these kinda of problems."
"> important: We're not aware of other libraries (except for fastai) that automatically and correctly apply data augmentation to coordinates. So if you're working with another library, you may need to disable data augmentation for these kind of problems."
]
},
{
@ -1664,7 +1664,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"This makes sense, since when coordinates are used a dependent variable, most of the time we're likely to be trying to predict something as close as possible; that's basically what `MSELoss` (mean-squared error loss) does. If you want to use a different loss function, you can pass it to `cnn_learner` using the `loss_func` parameter.\n",
"This makes sense, since when coordinates are used as dependent variable, most of the time we're likely to be trying to predict something as close as possible; that's basically what `MSELoss` (mean-squared error loss) does. If you want to use a different loss function, you can pass it to `cnn_learner` using the `loss_func` parameter.\n",
"\n",
"Note also that we didn't specify any metrics. That's because the MSE is already a useful metric for this task (although it's probably more interpretable after we take the square root). \n",
"\n",
@ -1858,7 +1858,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"In problems that are at first glance completely different (single-label classification, multi-label classification and regression) we end up using the same model with just different numbers of outputs. The different directions of those trainings is determined by the loss function, which is the one thing that changes. That's why it simportant to double-check your are using the right loss function for your problem.\n",
"In problems that are at first glance completely different (single-label classification, multi-label classification and regression) we end up using the same model with just different numbers of outputs. The different directions of those trainings is determined by the loss function, which is the one thing that changes. That's why its important to double-check your are using the right loss function for your problem.\n",
"\n",
"In fastai, the library will automatically try to pick the right one from the data you built, but if you are using pure PyTorch to build your `DataLoader`s, make sure you think hard when you have to decide on your loss function, and remember that you most probably want\n",
"\n",