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Fix a few typos in the documentation.

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Change-Id: I541db56b2b81ae758e233ce850d78c3cbb4b6fa3
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Martin Baeuml
2014-09-08 16:49:06 +02:00
parent 1aef66eeae
commit 7e43460d42
6 changed files with 36 additions and 36 deletions
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docs/source/tutorial.rst
+3 -3
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@@ -527,7 +527,7 @@ gives us:
Starting from parameter values :math:`m = 0, c=0` with an initial
objective function value of :math:`121.173` Ceres finds a solution
:math:`m= 0.291861, c = 0.131439` with an objective function value of
:math:`1.05675`. These values are a a bit different than the
:math:`1.05675`. These values are a bit different than the
parameters of the original model :math:`m=0.3, c= 0.1`, but this is
expected. When reconstructing a curve from noisy data, we expect to
see such deviations. Indeed, if you were to evaluate the objective
@@ -562,9 +562,9 @@ below. Notice how the fitted curve deviates from the ground truth.
:align: center
To deal with outliers, a standard technique is to use a
:class:`LossFunction`. Loss functions, reduce the influence of
:class:`LossFunction`. Loss functions reduce the influence of
residual blocks with high residuals, usually the ones corresponding to
outliers. To associate a loss function in a residual block, we change
outliers. To associate a loss function with a residual block, we change
.. code-block:: c++