spelling: that

Signed-off-by: Josh Soref <[email protected]>

Author: jsoref

_sass/vendors/neat/grid/_span-columns.scss

@@ -5,7 +5,7 @@
 /// @param {List} $span
 ///   A list containing `$columns`, the unitless number of columns the element spans (required), and `$container-columns`, the number of columns the parent element spans (optional).
 ///
-///   If only one value is passed, it is assumed that it's `$columns` and that that `$container-columns` is equal to `$grid-columns`, the total number of columns in the grid.
+///   If only one value is passed, it is assumed that it's `$columns` and that `$container-columns` is equal to `$grid-columns`, the total number of columns in the grid.
 ///
 ///   The values can be separated with any string such as `of`, `/`, etc.
 ///

gsoc/2017.md

@@ -338,7 +338,7 @@ Supervised by [@olafurpg](https://github.com/olafurpg)
 
 ###  Implementing a Benchmark Suite for Big Data and Machine Learning Applications
 
-In this project, the aim is to design and implement several larger Big Data and Machine Learning applications, which will be used to regularly test the performance of the Scala compiler releases, and to test the performance of JVMs running these applications. By the time the project is completed, you are expected to implement one larger data-intensive application at least for these frameworks: [Spark](https://spark.apache.org/), [Flink](https://flink.apache.org/), [Storm](https://storm.apache.org/), [Kafka](https://kafka.apache.org/) and [DeepLearning4j](https://github.com/deeplearning4j). Each of the applications will have to be accompanied with a dataset used to run the application.
+In this project, the aim is to design and implement several larger Big Data and Machine Learning applications, which will be used to regularly test the performance of the Scala compiler releases, and to test the performance of JVMs running these applications. By the time that the project is completed, you are expected to implement one larger data-intensive application at least for these frameworks: [Spark](https://spark.apache.org/), [Flink](https://flink.apache.org/), [Storm](https://storm.apache.org/), [Kafka](https://kafka.apache.org/) and [DeepLearning4j](https://github.com/deeplearning4j). Each of the applications will have to be accompanied with a dataset used to run the application.
 
 This project is an excellent opportunity to familiarize yourself with these modern cutting-edge frameworks for distributed computing and machine learning!