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| author | Martin Wicke <577277+martinwicke@users.noreply.github.com> | 2018-02-20 09:29:18 -0800 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2018-02-20 09:29:18 -0800 |
| commit | fb8fe7392808c26717388b784ae139c5618867e4 (patch) | |
| tree | 88b8eca35ae46c14531d9a6029b66b0f05923850 | |
| parent | bb63eb161db52e5a2b9000ccc6b8893e7ece0238 (diff) | |
| download | tensorflow-fb8fe7392808c26717388b784ae139c5618867e4.tar.gz | |
Grammar
| -rw-r--r-- | tensorflow/docs_src/mobile/mobile_intro.md | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/tensorflow/docs_src/mobile/mobile_intro.md b/tensorflow/docs_src/mobile/mobile_intro.md index c406c1852f2..69b63ae7d22 100644 --- a/tensorflow/docs_src/mobile/mobile_intro.md +++ b/tensorflow/docs_src/mobile/mobile_intro.md @@ -235,7 +235,7 @@ TensorFlow [on Github](https://github.com/tensorflow/models) that you can look through. Lean towards the simplest model you can find, and try to get started as soon as you have even a small amount of labelled data, since you’ll get the best results when you’re able to iterate quickly. The shorter the time it takes to -try training a model and running it in it's real application, the better overall +try training a model and running it in its real application, the better overall results you’ll see. It’s common for an algorithm to get great training accuracy numbers but then fail to be useful within a real application because there’s a mismatch between the dataset and real usage. Prototype end-to-end usage as soon |