Merge branch 'main' of https://github.com/fhdsl/AI_for_software into main

Author: jhudsl-robot

docs/03-writing_code.md

@@ -538,7 +538,7 @@ devtools::session_info()
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docs/04-refactoring.md

@@ -553,7 +553,7 @@ proc.time() - start_time
 
 ```
 ##    user  system elapsed 
-##  13.384   0.012  13.396
+##   9.212   0.008   9.219
 ```
 
 :::{.query}
@@ -581,7 +581,7 @@ proc.time() - start_time
 
 ```
 ##    user  system elapsed 
-##   0.833   0.348   0.795
+##   0.807   0.300   0.786
 ```
 
 The `outer()` function performs the same calculation as the nested loop in the original code, but more efficiently. It returns a matrix of all possible combinations of x and y values, with each element of the matrix being the product of the corresponding x and y values. The `rowSums()` function is then used to sum the elements of each row of the matrix, which is equivalent to summing the products of x and y for each index `i` in the original loop. This method avoids the need for the nested loop, resulting in a faster and more efficient computation.
@@ -609,7 +609,7 @@ proc.time() - start_time
 
 ```
 ##    user  system elapsed 
-##   0.394   0.355   0.483
+##   0.344   0.244   0.361
 ```
 
 One optimized way to perform the same calculation is by using the `%*%` operator to perform matrix multiplication. This can be done by converting x and y to matrices and transposing one of them so that their dimensions align for matrix multiplication. This code should be much faster than the original implementation because it takes advantage of highly optimized matrix multiplication algorithms in R.

docs/05-annotation.md

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docs/About.md

@@ -38,7 +38,7 @@ Tools Used:
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docs/about-the-authors.html

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docs/annotating-your-code.html

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docs/index.html

@@ -387,7 +387,7 @@ <h1>
 <div id="header">
 <h1 class="title">AI for Software Development</h1>
 <h2 class="subtitle"><em>Harnessing the Power of Large Language Models</em></h2>
-<p class="date"><em>July, 2023</em></p>
+<p class="date"><em>August, 2023</em></p>
 </div>
 <div id="about-this-course" class="section level1 unnumbered">
 <h1>About this Course</h1>
@@ -396,8 +396,8 @@ <h1>About this Course</h1>
 <h2>Available course formats</h2>
 <p>This course is available in multiple formats which allows you to take it in the way that best suits your needs.</p>
 <ul>
-<li>The material for this course can be viewed without login requirement on this <a href="hutchdatascience.org/AI_for_software/">Bookdown website</a>. This format might be most appropriate for you if you rely on screen-reader technology.
-<!-- - This course can be taken for [free certification through Leanpub](LINK HERE). -->
+<li>The material for this course can be viewed without login requirement on this <a href="https://hutchdatascience.org/AI_for_software">Bookdown website</a>. This format might be most appropriate for you if you rely on screen-reader technology.</li>
+<li>This course can be taken for <a href="https://leanpub.com/courses/fredhutch/ai_for_software">free certification through Leanpub</a>.
 <!-- - This course can be taken on [Coursera for certification here](LINK HERE) (but it is not available for free on Coursera). --></li>
 <li>Our courses are open source, you can find the <a href="https://github.com/fhdsl/AI_for_software/">source material for this course on GitHub</a>.</li>
 <li>The slides for the images in the course can be found <a href="https://docs.google.com/presentation/d/1MCNeSO4aOm1iESWDLOGTcx3aLEbnu8UttV0QGVAeafE/edit?usp=sharing">here</a>.</li>

docs/index.md

@@ -1,7 +1,7 @@
 ---
 title: "AI for Software Development"
 subtitle: "Harnessing the Power of Large Language Models"
-date: "July, 2023"
+date: "August, 2023"
 site: bookdown::bookdown_site
 documentclass: book
 bibliography: [book.bib]
@@ -19,8 +19,8 @@ This course on AI for software development explores the use of AI large language
 
 This course is available in multiple formats which allows you to take it in the way that best suits your needs.
 
-- The material for this course can be viewed without login requirement on this [Bookdown website]( hutchdatascience.org/AI_for_software/). This format might be most appropriate for you if you rely on screen-reader technology.
-<!-- - This course can be taken for [free certification through Leanpub](LINK HERE). -->
+- The material for this course can be viewed without login requirement on this [Bookdown website](https://hutchdatascience.org/AI_for_software). This format might be most appropriate for you if you rely on screen-reader technology.
+- This course can be taken for [free certification through Leanpub](https://leanpub.com/courses/fredhutch/ai_for_software).
 <!-- - This course can be taken on [Coursera for certification here](LINK HERE) (but it is not available for free on Coursera). -->
 - Our courses are open source, you can find the [source material for this course on GitHub](https://github.com/fhdsl/AI_for_software/).
 - The slides for the images in the course can be found [here](https://docs.google.com/presentation/d/1MCNeSO4aOm1iESWDLOGTcx3aLEbnu8UttV0QGVAeafE/edit?usp=sharing).