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typos fixed in ch15 and ch16

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Rubens 2020-03-18 15:24:13 -03:00
parent f0eeac513a
commit 000a66b499
2 changed files with 62 additions and 4 deletions
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33
15_arch_details.ipynb
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@ -740,7 +740,7 @@
"\n", "\n",
"Once you've got as much data as you think you can reasonably get a hold of, and are using it as effectively as possible by taking advantage of all of the labels that you can find, and all of the augmentation that make sense, if you are still overfitting and you should think about using more generalisable architectures. For instance, adding batch normalisation may improve generalisation.\n", "Once you've got as much data as you think you can reasonably get a hold of, and are using it as effectively as possible by taking advantage of all of the labels that you can find, and all of the augmentation that make sense, if you are still overfitting and you should think about using more generalisable architectures. For instance, adding batch normalisation may improve generalisation.\n",
"\n", "\n",
"If you are still overfitting after doing the best you can at using your data and tuning your architecture, then you can take a look at regularisation. Generally speaking, adding dropout to the last layer or two will do a good job of regularising your model. However, as we learnt from the story of the development of AWD-LSTM, it is often the case that adding dropout of different types throughout your model can help regularise even better. Generally speaking, a larger model with more regularisation is more flexible, and can therefore be more accurate, and a smaller model with less regularisation.\n", "If you are still overfitting after doing the best you can at using your data and tuning your architecture, then you can take a look at regularisation. Generally speaking, adding dropout to the last layer or two will do a good job of regularising your model. However, as we learnt from the story of the development of AWD-LSTM, it is often the case that adding dropout of different types throughout your model can help regularise even better. Generally speaking, a larger model with more regularisation is more flexible, and can therefore be more accurate than a smaller model with less regularisation.\n",
"\n", "\n",
"Only after considering all of these options would be recommend that you try using smaller versions of your architectures." "Only after considering all of these options would be recommend that you try using smaller versions of your architectures."
] ]
@ -821,7 +821,36 @@
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33
16_accel_sgd.ipynb
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@ -361,7 +361,7 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"opt_func = partial(Optimizer, cbs=[sgd_step])" "opt_func = partial(Optimizer, cbs=[sgd_cb])"
] ]
}, },
{ {
@ -1318,7 +1318,36 @@
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