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authorMelissa Weber Mendonça <melissawm@gmail.com>2020-01-24 10:44:00 -0300
committerGitHub <noreply@github.com>2020-01-24 10:44:00 -0300
commit7582faace489302579fd4137a14bca60f25a8d65 (patch)
treee81ee141cd1ef7e55ebb217ca720078a0e34b318
parent3d889a88f058cd81718fd0010d17e104db71858f (diff)
downloadnumpy-7582faace489302579fd4137a14bca60f25a8d65.tar.gz
Update doc/source/user/tutorial-svd.rst
Co-Authored-By: Anne Bonner <35413198+bonn0062@users.noreply.github.com>
-rw-r--r--doc/source/user/tutorial-svd.rst2
1 files changed, 1 insertions, 1 deletions
diff --git a/doc/source/user/tutorial-svd.rst b/doc/source/user/tutorial-svd.rst
index 9c7b9dd6f..680322cbb 100644
--- a/doc/source/user/tutorial-svd.rst
+++ b/doc/source/user/tutorial-svd.rst
@@ -392,7 +392,7 @@ integers)." This is expected from the manipulation we just did on the original
image.
Now, to do the approximation, we must choose only the first ``k`` singular
-values for each color channel. This can be done by the following syntax::
+values for each color channel. This can be done using the following syntax::
>>> approx_img = U @ Sigma[..., :k] @ Vt[..., :k, :]