Dark mode + fix typos/grammer

.gitignore

@@ -1,5 +1,26 @@
+# Environments
 /env/
+
+# Editors
+.idea/
+.vscode/
+
+# Generated files from Quarto
 /.quarto/
 /_book/
+
+index.tex
+index.aux
+index.pdf
+index.toc
+index_files/
+index.log
+site_libs
+/*.html
+/*.ipynb
+/*.epub
+**/figure-latex/*
+
+# macOS
 .DS_Store
-.idea/
+*.icloud

_quarto.yml

@@ -119,9 +119,16 @@ crossref:
 format:
   html:
     theme: 
-      - cosmo
-      - style.scss
+      light:
+        - cosmo
+        - style.scss
+        - style-light.scss
+      dark:
+        - darkly
+        - style.scss
+        - style-dark.scss
     mainfont: Nunito
+    highlight-style: github
     code-link: true
     link-external-icon: true
     link-external-newwindow: true

data_engineering.qmd

@@ -16,7 +16,7 @@ Data is the lifeblood of AI systems. Without good data, even the most advanced m
 
 * Briefly learn different methods for storing and managing data such as databases, data warehouses, and data lakes.
 
-* Comprehend the role of transparency through metadata and dataset documentation, as well as tracking data provenance to faciltate ethics, auditing, and reproducibility.
+* Comprehend the role of transparency through metadata and dataset documentation, as well as tracking data provenance to facilitate ethics, auditing, and reproducibility.
 
 * Understand how licensing protocols govern legal data access and usage, necessitating careful compliance.
 
@@ -155,7 +155,7 @@ Web scraping can yield inconsistent or inaccurate data. For example, the photo i
 
 Crowdsourcing for datasets is the practice of obtaining data by using the services of a large number of people, either from a specific community or the general public, typically via the internet. Instead of relying on a small team or specific organization to collect or label data, crowdsourcing leverages the collective effort of a vast, distributed group of participants. Services like Amazon Mechanical Turk enable the distribution of annotation tasks to a large, diverse workforce. This facilitates the collection of labels for complex tasks like sentiment analysis or image recognition that specifically require human judgment.
 
-Crowdsourcing has emerged as an effective approach for many data collection and problem-solving needs. One major advantage of crowdsourcing is scalability—by distributing tasks to a large, global pool of contributors on digital platforms, projects can process huge volumes of data in a short timeframe. This makes crowdsourcing ideal for large-scale data labeling, collection, and analysis.
+Crowdsourcing has emerged as an effective approach for many data collection and problem-solving needs. One major advantage of crowdsourcing is scalability—by distributing tasks to a large, global pool of contributors on digital platforms, projects can process huge volumes of data in a short time frame. This makes crowdsourcing ideal for large-scale data labeling, collection, and analysis.
 
 In addition, crowdsourcing taps into a diverse group of participants, bringing a wide range of perspectives, cultural insights, and language abilities that can enrich data and enhance creative problem-solving in ways that a more homogenous group may not. Because crowdsourcing draws from a large audience beyond traditional channels, it also tends to be more cost-effective than conventional methods, especially for simpler microtasks.
 

frameworks.qmd

@@ -657,7 +657,7 @@ We can also use ML to improve ML frameworks in the future. Some current uses of
 
 * neural architecture search (NAS) to automatically search for optimal network architectures
 
-* AutoML, which as described in the [Advanced Features][@sec-ai_frameworks-advanced] section, automates the ML pipeline.
+* AutoML, which as described in @sec-ai_frameworks-advanced, automates the ML pipeline.
 
 ## Conclusion
 

index.qmd

@@ -1,8 +1,8 @@
 # Preface {.unnumbered}
 
-Welcome to {{< var title.long >}} This book is your gateway to the fast-paced world of artificial intelligence through the lens of embedded systems. It as an extension of the foundational course, tinyML from [CS249r](https://sites.google.com/g.harvard.edu/cs249-tinyml-2023) at Harvard University.
+Welcome to {{< var title.long >}} This book is your gateway to the fast-paced world of artificial intelligence through the lens of embedded systems. It is an extension of the foundational course, tinyML from [CS249r](https://sites.google.com/g.harvard.edu/cs249-tinyml-2023) at Harvard University.
 
-Our aim is to make this open-soruce book a collaborative effort that brings together insights from students, professionals, and the broader community of applied machine learning practitioners. We want to create a one-stop guide that dives deep into the nuts and bolts of AI systems and its many uses.
+Our aim is to make this open-source book a collaborative effort that brings together insights from students, professionals, and the broader community of applied machine learning practitioners. We want to create a one-stop guide that dives deep into the nuts and bolts of AI systems and their many uses.
 
 > "If you want to go fast, go alone. If you want to go far, go together."
 > -- African Proverb

optimizations.qmd

@@ -510,11 +510,9 @@ Calibration is the process of selecting the most effective clipping range [$\alp
 
 There are many calibration methods but a few commonly used include:
 
-Max: Use the maximum absolute value seen during calibration. However, this method is susceptible to outlier data.
-
-Entropy: Use KL divergence to minimize information loss between the original floating-point values and values that could be represented by the quantized format. This is the default method used by TensorRT.
-
-Percentile: Set the range to a percentile of the distribution of absolute values seen during calibration. For example, 99% calibration would clip 1% of the largest magnitude values.
+* Max: Use the maximum absolute value seen during calibration. However, this method is susceptible to outlier data.
+* Entropy: Use KL divergence to minimize information loss between the original floating-point values and values that could be represented by the quantized format. This is the default method used by TensorRT.
+* Percentile: Set the range to a percentile of the distribution of absolute values seen during calibration. For example, 99% calibration would clip 1% of the largest magnitude values.
 
 ![Histogram of input activations to layer 3 in ResNet50 and calibrated ranges (@intquantfordeepinf).](images/efficientnumerics_calibrationcopy.png)
 

style-dark.scss

@@ -0,0 +1,23 @@
+/*-- scss:defaults --*/
+
+$primary: rgb(27, 27, 27) !default;
+$success: #E09F9C !default;
+
+/*-- scss:rules --*/
+
+.sidebar-title {
+  color: $primary;
+}
+
+/* Headings ------------------------------------------------------ */
+
+.quarto-section-identifier {
+  color: #6C6C6C;
+}
+
+/* Code ------------------------------------------------ */
+
+:not(pre) > code {
+  color: #e2e3e5;
+}
+

style-light.scss

@@ -0,0 +1,24 @@
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+
+$primary: #A51C30 !default;
+
+$background-color: #bfe4eb !default;
+
+/*-- scss:rules --*/
+
+.sidebar-title {
+  color: $primary;
+}
+
+/* Headings ------------------------------------------------------ */
+
+.quarto-section-identifier {
+  color: #6C6C6C;
+}
+
+/* Code ------------------------------------------------ */
+
+:not(pre) > code {
+  color: #373a3c;
+}
+

style.scss

@@ -1,23 +1,13 @@
 /*-- scss:defaults --*/
 
-$primary: #A51C30 !default;
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 @import url('https://fonts.googleapis.com/css2?family=Nunito:wght@400;800&display=swap');
 
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-$background-color: #bfe4eb !default;
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-
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 div.sidebar-item-container .active {
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@@ -26,9 +16,6 @@ div.sidebar-item-container .active {
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-img.quarto-cover-image {
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 #title-block-header.quarto-title-block.default .quarto-title h1.title {
@@ -46,21 +33,11 @@ h4 { margin-top: 1.5em; font-size: 1.1rem; }
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 /* Code ------------------------------------------------ */
 
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