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Sylvain Gugger 2020-04-15 08:26:19 -04:00 committed by GitHub
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.gitignore vendored
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*.bak
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01_intro.ipynb
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02_production.ipynb
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"source": [
"We've seen that deep learning can solve a lot of challenging problems quickly and with little code. As a beginner there's a sweet spot of problems that are similar enough to our example problems that you can very quickly get extremely useful results. However, deep learning isn't magic! The same 5 lines of code won't work on every problem anyone can think of today. Underestimating the constraints and overestimating the capabilities of deep learning may lead to frustratingly poor results. At least until you gain some experience to solve the problems that arise. Overestimating the constraints and underestimating the capabilities of deep learning may mean you do not attempt a solvable problem because you talk yourself out of it. \n",
"\n",
"We often talk to people who overestimate both the constraints, and the capabilities of deep learning. Both of these can be problems: underestimating the capabilities means that you might not even try things which could be very beneficial; underestimating the constraints might mean that you fail to consider and react to important issues.\n",
"We often talk to people who underestimate both the constraints, and the capabilities of deep learning. Both of these can be problems: underestimating the capabilities means that you might not even try things which could be very beneficial; underestimating the constraints might mean that you fail to consider and react to important issues.\n",
"\n",
"The best thing to do is to keep an open mind. If you remain open to the possibility that deep learning might solve part of your problem with less data or complexity than you expect, then it is possible to design a process where you can find the specific capabilities and constraints related to your particular problem as you work through the process. This doesn't mean making any risky bets — we will show you how you can gradually roll out models so that they don't create significant risks, and can even backtest them prior to putting them in production.\n",
"\n",
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11_midlevel_data.ipynb
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12_nlp_dive.ipynb
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" self.i_h = nn.Embedding(vocab_sz, n_hidden)\n",
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" self.h_o = nn.Linear(n_hidden, vocab_sz)\n",
" self.h = [torch.zeros(2, bs, n_hidden) for _ in range(n_layers)]\n",
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clean/10_nlp.ipynb
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clean/16_accel_sgd.ipynb
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clean/17_foundations.ipynb
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},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,

1
settings.ini
View File

@ -21,4 +21,5 @@ lib_path = fastbook
title = fastbook
doc_host = https://fastai.github.io
doc_baseurl = /fastbook/
host = github