fixed a number of typos in docs/FAQ
@@ -15,7 +15,7 @@ Should I normalise my features?
The default answer is yes, but, of course, the real answer is
"it depends". If your features have meaningful relationships
-with one another (say, latitude and longitude vales) then
+with one another (say, latitude and longitude values) then
normalising per feature is not a good idea. For features that
are essentially independent it does make sense to get all the
features on (relatively) the same scale. The best way to do
@@ -73,7 +73,7 @@ bokeh is similarly useful (but does not need to be quite so small).
More generally the real solution, particular with large datasets,
is to use `datashader <http://datashader.org/>`_ for plotting.
-Datashader is a plotting llibrary that handles aggregation
+Datashader is a plotting library that handles aggregation
of large scale data in scatter plots in a way that can better
show the underlying detail that can otherwise be lost. We
highly recommend investing the time to learn datashader for
@@ -113,7 +113,7 @@ be added.
There is a UMAP implementation for GPU available in
the NVIDIA RAPIDS cuML library, so if you need GPU
-support that is currently the best palce to go.
+support that is currently the best place to go.
Can I add a custom loss function?
---------------------------------
@@ -122,7 +122,7 @@ To allow for fast performance the SGD phase of UMAP has
been hand-coded for the specific needs of UMAP. This makes
custom loss functions a little difficult to handle. Now
that Numba (as of version 0.38) supports passing functions
-it is posisble that future versions of UMAP may support
+it is possible that future versions of UMAP may support
such functionality. In the meantime you should definitely
look into `smallvis <https://github.com/jlmelville/smallvis>`_,
a library for t-SNE, LargeVis, UMAP, and related algorithms.
@@ -198,7 +198,7 @@ This is an example of an embedding for a popular Fashion MNIST dataset.
Note that FMNIST is mostly a toy dataset (MNIST on steroids).
On such a simplistic case UMAP shows distillation results
-(i.e. if we use its embedding in a downsteam task like classification)
+(i.e. if we use its embedding in a downstream task like classification)
comparable to VAEs, which are more computationally expensive.
By definition:
@@ -238,7 +238,7 @@ Which tool should I use?
Where can I learn more?
-- While PCA is ubiqutous, you may `look <https://github.com/snakers4/playing_with_vae>`_
+- While PCA is ubiquitous, you may `look <https://github.com/snakers4/playing_with_vae>`_
at this example comparing PCA / UMAP / VAEs;
Successful use-cases
