Fix Typo

Author: flegaspi700

5-Data-Science-In-Cloud/18-Low-Code/README.md

@@ -157,7 +157,7 @@ Some key factors are to consider when creating a compute resource and those choi
 
 A CPU (Central Processing Unit) is the electronic circuitry that executes instructions comprising a computer program. A GPU (Graphics Processing Unit) is a specialized electronic circuit that can execute graphics-related code at a very high rate. 
 
-The main difference between CPU and GPU architecture is that a CPU is designed to handle a wide-range of tasks quickly (as measured by CPU clock speed), but are limited in the concurrency of tasks that can be running. GPUs are designed for parallel computing and therfore are much better at deep learning tasks.
+The main difference between CPU and GPU architecture is that a CPU is designed to handle a wide-range of tasks quickly (as measured by CPU clock speed), but are limited in the concurrency of tasks that can be running. GPUs are designed for parallel computing and therefore are much better at deep learning tasks.
 
 | CPU                                     | GPU                         |
 |-----------------------------------------|-----------------------------|

6-Data-Science-In-Wild/20-Real-World-Examples/README.md

@@ -51,7 +51,7 @@ Let's look at one example - the [MIT Gender Shades Study](http://gendershades.or
 
  * **What:** The objective of the research project was to _evaluate bias present in automated facial analysis algorithms and datasets_ based on gender and skin type. 
  * **Why:** Facial analysis is used in areas like law enforcement, airport security, hiring systems and more - contexts where inaccurate classifications (e.g., due to bias) can cause potential economic and social harms to affected individuals or groups. Understanding (and eliminating or mitigating) biases is key to fairness in usage.
- * **How:** Researchers recongized that existing benchmarks used predominantly lighter-skinned subjects, and curated a new data set (1000+ images) that was _more balanced_ by gender and skin type. The data set was used to evaluate the accuracy of three gender classification products (from Microsoft, IBM & Face++). 
+ * **How:** Researchers recognized that existing benchmarks used predominantly lighter-skinned subjects, and curated a new data set (1000+ images) that was _more balanced_ by gender and skin type. The data set was used to evaluate the accuracy of three gender classification products (from Microsoft, IBM & Face++). 
 
 Results showed that though overall classification accuracy was good, there was a noticeable difference in error rates between various subgroups - with **misgendering** being higher for females or persons with darker skin types, indicative of bias.