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<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>Mathematics in Machine Learning & Data Science</title>
<meta charset="utf-8" />
<meta name="author" content="Sothea Has" />
<script src="libs/header-attrs-2.26/header-attrs.js"></script>
<link href="libs/htmltools-fill-0.5.8.1/fill.css" rel="stylesheet" />
<script src="libs/htmlwidgets-1.6.4/htmlwidgets.js"></script>
<script src="libs/plotly-binding-4.10.4/plotly.js"></script>
<script src="libs/typedarray-0.1/typedarray.min.js"></script>
<script src="libs/jquery-3.5.1/jquery.min.js"></script>
<link href="libs/crosstalk-1.2.1/css/crosstalk.min.css" rel="stylesheet" />
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<link href="libs/plotly-htmlwidgets-css-2.11.1/plotly-htmlwidgets.css" rel="stylesheet" />
<script src="libs/plotly-main-2.11.1/plotly-latest.min.js"></script>
<link rel="stylesheet" href="xaringan-themer.css" type="text/css" />
</head>
<body>
<textarea id="source">
name: layout-general
layout: true
class: left, top
---
count: false
background-image: url('./img/bg_bd2.jpg')
background-size: cover
class: top, center, title-slide
<img src="img/X.png" align="middle" height="110" width="100"> .white[........]
<img src="img/logo_lmd.png" align="right" height="90">
<img src="img/logo_ens-remove.png" align="left" height="90">
<br><hbr>
<hr class="L1"> <hbr>
## Mathematics in Machine Learning<br> & Data Science
<hr class="L2">
#### 🥳Celebrating the 73rd Birthday of T. SOUN Sovann🥳
<img src="img/RUPP_logo.png" align="middle" height="80"> <hbr>
### Sothea .textsc[Has], PhD
---
# A bit of motivation 💪😎
.left[
<img src="img/job1.png" align="middle" height="150">
<img src="img/job3.png" align="middle" height="150">
]
<br>
.left[
<img src="img/job2.png" align="middle" height="235">
<img src="img/job4.png" align="middle" height="235">
]
---
template: inter-slide
class: left, middle
count: true
## <svg aria-hidden="true" role="img" viewBox="0 0 576 512" style="height:1em;width:1.12em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:rgb(16, 111, 171);;overflow:visible;position:relative;"><path d="M565.6 36.2C572.1 40.7 576 48.1 576 56V392c0 10-6.2 18.9-15.5 22.4l-168 64c-5.2 2-10.9 2.1-16.1 .3L192.5 417.5l-160 61c-7.4 2.8-15.7 1.8-22.2-2.7S0 463.9 0 456V120c0-10 6.1-18.9 15.5-22.4l168-64c5.2-2 10.9-2.1 16.1-.3L383.5 94.5l160-61c7.4-2.8 15.7-1.8 22.2 2.7zM48 136.5V421.2l120-45.7V90.8L48 136.5zM360 422.7V137.3l-144-48V374.7l144 48zm48-1.5l120-45.7V90.8L408 136.5V421.2z"/></svg> .bold-blue[Outline]
<br>
.hhead[I. What's Machine Learning (ML) & Data Science (DS)?]
<br>
.hhead[II. Why is Mathematics important in ML & DS?]
<br>
.hhead[III. Few words from me, maybe it's the beginning for you!]
---
template: inter-slide
class: left, middle
count: false
## <svg aria-hidden="true" role="img" viewBox="0 0 576 512" style="height:1em;width:1.12em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:rgb(16, 111, 171);;overflow:visible;position:relative;"><path d="M565.6 36.2C572.1 40.7 576 48.1 576 56V392c0 10-6.2 18.9-15.5 22.4l-168 64c-5.2 2-10.9 2.1-16.1 .3L192.5 417.5l-160 61c-7.4 2.8-15.7 1.8-22.2-2.7S0 463.9 0 456V120c0-10 6.1-18.9 15.5-22.4l168-64c5.2-2 10.9-2.1 16.1-.3L383.5 94.5l160-61c7.4-2.8 15.7-1.8 22.2 2.7zM48 136.5V421.2l120-45.7V90.8L48 136.5zM360 422.7V137.3l-144-48V374.7l144 48zm48-1.5l120-45.7V90.8L408 136.5V421.2z"/></svg> .bold-blue[Outline]
<br>
.section[I. What's Machine Learning (ML) & Data Science (DS)?]
<br>
.hhead[II. Why is Mathematics important in ML & DS?]
<br>
.hhead[III. Few words from me, maybe it's the beginning for you!]
---
# What's Machine Learning? 🤔
- A field of study that focuses on the development of algorithms and statistical models that enable computers to learn from and make predictions or decisions based on data, without being explicitly programmed for each task.
<hbr>
.center[
<img src="img/venn_ml.jpg" align="middle" height="400">
<h0br>
]
---
count: false
# What's Machine Learning? 🤔
- A field of study that focuses on the development of algorithms and statistical models that enable computers to learn from and make predictions or decisions based on data, without being explicitly programmed for each task.
<hbr>
.pull-left-60[
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]
.pull-right-40[
| ID|Gender | Height| Weight|
|--:|:------|------:|------:|
| 1|Male | 1.72| 62.5|
| 2|Male | 1.65| 55.0|
| 3|Male | 1.64| 53.0|
| 4|Male | 1.71| 63.0|
| 5|Male | 1.66| 57.4|
| 6|Male | 1.70| 56.0|
| 7|Female | 1.56| 38.0|
| 8|Male | 1.65| 50.0|
]
---
count: true
# What's Machine Learning? 🤔
- A field of study that focuses on the development of algorithms and statistical models that enable computers to learn from and make predictions or decisions based on data, without being explicitly programmed for each task.
<hbr>
.pull-left-60[
<div class="plotly html-widget html-fill-item" id="htmlwidget-2251ae367503db46e739" style="width:504px;height:360px;"></div>
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]
.pull-right-40[
- Relationship between .stress[Height] & .stress[Weight]?
- What can .stress[Height] & .stress[Weight] tell about .stress[Gender]?
- Standard .stress[Height] & .stress[Weight] in general?
> .stress[Notation]:
- Input, explanatory variables: `\(X_1,X_2,...\)`
- Output, target variable: `\(Y\)`.
]
---
exclude:true
count: false
# What's Machine Learning? 🤔
- A field of study that focuses on the development of algorithms and statistical models that enable computers to learn from and make predictions or decisions based on data, without being explicitly programmed for each task.
<hbr>
.pull-left-60[
<div class="plotly html-widget html-fill-item" id="htmlwidget-6f911c383ceddadbecaa" style="width:504px;height:360px;"></div>
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]
.pull-right-40[
- Relationship between .stress[Height] & .stress[Weight]?
- What can .stress[Height] & .stress[Weight] tell about .stress[Gender]?
- Standard .stress[Height] & .stress[Weight] in general?
]
.pull-right-40[<h1br>
> .stress[Notation]:
- Input, explanatory variables: `\(X_1,X_2,...\)`
- Output, target variable: `\(Y\)`.
]
---
count: false
# What's Machine Learning? 🤔
- A field of study that focuses on the development of algorithms and statistical models that enable computers to learn from and make predictions or decisions based on data, without being explicitly programmed for each task.
<br>
.pull-left[<hbr>
<img src="img/ml_model.jpg" align="middle" width="450">
]
.pull-right[
<img src="img/MLElements.gif" align="middle" width="500">
]
---
count: true
# What's Machine Learning? 🤔
- A field of study that focuses on the development of algorithms and statistical models that enable computers to learn from and make predictions or decisions based on data, without being explicitly programmed for each task.
<br>
.pull-left[<hbr>
<img src="img/ml_model1.jpg" align="middle" width="450">
]
.pull-right[
<img src="img/MLElements.gif" align="middle" width="500">
]
---
count: true
# What's Machine Learning? 🤔
- A field of study that focuses on the development of algorithms and statistical models that enable computers to learn from and make predictions or decisions based on data, without being explicitly programmed for each task.
<br>
.pull-left[<hbr>
<img src="img/ml_model2.jpg" align="middle" width="450">
]
.pull-right[
<img src="img/MLElements.gif" align="middle" width="500">
]
---
count: true
# What's Machine Learning? 🤔
- A field of study that focuses on the development of algorithms and statistical models that enable computers to learn from and make predictions or decisions based on data, without being explicitly programmed for each task.
<br>
.pull-left[<hbr>
<img src="img/ml_model3.jpg" align="middle" width="450">
]
.pull-right[
<img src="img/MLElements.gif" align="middle" width="500">
]
---
count: true
# What's Machine Learning? 🤔
- A field of study that focuses on the development of algorithms and statistical models that enable computers to learn from and make predictions or decisions based on data, without being explicitly programmed for each task.
<br>
.pull-left[<hbr>
<img src="img/ml_model4.jpg" align="middle" width="450">
]
.pull-right[
<img src="img/MLElements.gif" align="middle" width="500">
]
---
# What's Data Science? 🤔
- A field of study that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from potentially noisy, structured, or unstructured data.
<hbr>
.center[
<img src="img/venn_ds.jpg" align="middle" height="350">
]
---
exclude:true
# What's Data Science? 🤔
- A field of study that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from potentially noisy, structured, or unstructured data.
<hbr>
.center[
<img src="img/chart_ds.png" align="middle" height="410">
]
---
template: inter-slide
class: left, middle
count: true
## <svg aria-hidden="true" role="img" viewBox="0 0 576 512" style="height:1em;width:1.12em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:rgb(16, 111, 171);;overflow:visible;position:relative;"><path d="M565.6 36.2C572.1 40.7 576 48.1 576 56V392c0 10-6.2 18.9-15.5 22.4l-168 64c-5.2 2-10.9 2.1-16.1 .3L192.5 417.5l-160 61c-7.4 2.8-15.7 1.8-22.2-2.7S0 463.9 0 456V120c0-10 6.1-18.9 15.5-22.4l168-64c5.2-2 10.9-2.1 16.1-.3L383.5 94.5l160-61c7.4-2.8 15.7-1.8 22.2 2.7zM48 136.5V421.2l120-45.7V90.8L48 136.5zM360 422.7V137.3l-144-48V374.7l144 48zm48-1.5l120-45.7V90.8L408 136.5V421.2z"/></svg> .bold-blue[Outline]
<br>
.hhead[I. What's Machine Learning (ML) & Data Science (DS)?]
<br>
.section[II. Why is Mathematics important in ML & DS?]
<br>
.hhead[III. Few words from me, maybe the beginning for you!]
---
# Why is Math important? 🤔
.left[
- Just as a building can never exist without a foundation, neither can ML and DS without math!
]
--
<h0br>
.pull-left[
<img src="img/digit_7.png" align="middle" height="200">
<img src="img/digit.gif" align="middle" height="200">
]
.pull-right[
<img src="img/multi_logit.gif" align="middle" height="200">
<img src="img/cnn_white.gif" align="middle" height="200">
]
---
# Why is Math important? 🤔
.left[
- Just as a building can never exist without a foundation, neither can ML and DS without math!
]
<h0br>
.pull-left[<h1br>
## Data ⛽ <hbr>
- `\(X,Y\)` are .stress[random], often represented as .stress[vector] or .stress[matrix] ...
- Measure theory/probability
- Statistics
- Linear algebra ... <h2br>
<img src="img/digit.gif" align="middle" height="200">
]
.pull-right[
<img src="img/multi_logit.gif" align="middle" height="200">
<img src="img/cnn_white.gif" align="middle" height="200">
]
---
count: true
# Why is Math important? 🤔
.left[
- Just as a building can never exist without a foundation, neither can ML and DS without math!
]
<h0br>
.pull-left[<h1br>
## Data ⛽ <hbr>
- `\(X,Y\)` are .stress[random], often represented as .stress[vector] or .stress[matrix] ...
- Measure theory/probability
- Statistics
- Linear algebra ... <h1br>
## Model <h4br> .tab[<img src="img/neural.png" align="middle" height="50">] <hbr>
- Simply just a (non/parametric) function `\(f_{\theta}:{\cal X}\to {\cal Y}\)`,
`$$f_{\theta}(x_i)=\hat{y}_i\approx y_i,\forall i=1,2,...,N$$`
]
.pull-right[
<img src="img/multi_logit.gif" align="middle" height="200">
<img src="img/cnn_white.gif" align="middle" height="200">
]
---
count: false
# Why is Math important? 🤔
.left[
- Just as a building can never exist without a foundation, neither can ML and DS without math!
]
<h0br>
.pull-left[<h1br>
## Data ⛽ <hbr>
- `\(X,Y\)` are .stress[random], often represented as .stress[vector] or .stress[matrix] ...
- Measure theory/probability
- Statistics
- Linear algebra ... <h1br>
## Model <h4br> .tab[<img src="img/neural.png" align="middle" height="50">] <hbr>
- Simply just a (non/parametric) function `\(f_{\theta}:{\cal X}\to {\cal Y}\)`,
`$$f_{\theta}(x_i)=\hat{y}_i\approx y_i,\forall i=1,2,...,N$$`
]
.pull-right[<h1br>
## Loss 📏 <hbr>
- Notion of .stress[closeness]: `\(``\hat{y}\approx y"\)`.
- Distances/metrics
- Bregman divergences
- Topological structures ... <h3br>
<img src="img/cnn_white.gif" align="middle" height="200">
]
---
count: true
# Why is Math important? 🤔
.left[
- Just as a building can never exist without a foundation, neither can ML and DS without math!
]
<h0br>
.pull-left[<h1br>
## Data ⛽ <hbr>
- `\(X,Y\)` are .stress[random], often represented as .stress[vector] or .stress[matrix] ...
- Measure theory/probability
- Statistics
- Linear algebra ... <h1br>
## Model <h4br> .tab[<img src="img/neural.png" align="middle" height="50">] <hbr>
- Simply just a (non/parametric) function `\(f_{\theta}:{\cal X}\to {\cal Y}\)`,
`$$f_{\theta}(x_i)=\hat{y}_i\approx y_i,\forall i=1,2,...,N$$`
]
.pull-right[<h1br>
## Loss 📏 <hbr>
- Notion of .stress[closeness]: `\(``\hat{y}\approx y"\)`.
- Distances/metrics
- Bregman divergences
- Topological structures ... <hbr>
## Feedback/learning <hbr>
- Improving the state of the model ...
- Real analysis/vector calculus
- Numerical & Complexity analysis
- Optimization methods ...
]
---
# Some literatures 📃 <hbr>
.pull-left-60[
[<img src="img/paper5.png" align="middle" height="200">
<img src="img/paper6.png" align="middle" height="200">](https://web.njit.edu/~usman/courses/cs675_fall18/10.1.1.441.7873.pdf)
]
.center[
.pull-right-40[<h1br>
[<img src="img/paper3.png" align="middle" weight="100%">](https://arxiv.org/pdf/2112.15210)
[<img src="img/paper2.png" align="middle" weight="100%">](https://proceedings.mlr.press/v162/hashimoto22a/hashimoto22a.pdf)
]
]
---
# A toy example of Neural Network <hbr>
<iframe src='https://playground.tensorflow.org/#activation=tanh&batchSize=20&dataset=circle&regDataset=reg-plane&learningRate=0.03&regularizationRate=0&noise=20&networkShape=4,2&seed=0.39787&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false' style="width:100%; height:470px;" border: 0px none; title="A toy example of NN">
</iframe>
.stress[Source: [Daniel Smilkov and Shan Carter](https://playground.tensorflow.org/#activation=tanh&batchSize=20&dataset=circle&regDataset=reg-plane&learningRate=0.03&regularizationRate=0&noise=20&networkShape=4,2&seed=0.39787&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false)]
---
template: inter-slide
class: left, middle
count: true
## <svg aria-hidden="true" role="img" viewBox="0 0 576 512" style="height:1em;width:1.12em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:rgb(16, 111, 171);;overflow:visible;position:relative;"><path d="M565.6 36.2C572.1 40.7 576 48.1 576 56V392c0 10-6.2 18.9-15.5 22.4l-168 64c-5.2 2-10.9 2.1-16.1 .3L192.5 417.5l-160 61c-7.4 2.8-15.7 1.8-22.2-2.7S0 463.9 0 456V120c0-10 6.1-18.9 15.5-22.4l168-64c5.2-2 10.9-2.1 16.1-.3L383.5 94.5l160-61c7.4-2.8 15.7-1.8 22.2 2.7zM48 136.5V421.2l120-45.7V90.8L48 136.5zM360 422.7V137.3l-144-48V374.7l144 48zm48-1.5l120-45.7V90.8L408 136.5V421.2z"/></svg> .bold-blue[Outline]
<br>
.hhead[I. What's Machine Learning (ML) & Data Science (DS)?]
<br>
.hhead[II. Why is Mathematics important in ML & DS?]
<br>
.section[III. Few words from me, maybe it's the beginning for you!]
---
count: true
# A few words 🤓 <hbr>
#### `\(\bullet\)` Mathematics is the foundation of everything.
--
#### `\(\bullet\)` It's the universal language and valuable asset.
--
#### `\(\bullet\)` Mastering it unlocks countless opportunities.
--
#### `\(\bullet\)` What's Math to you?
--
<hbr>
.pull-right-60[ <h1br>
# Some advices 🔑 <hbr>
#### `\(\bullet\)` Reevaluate how you perceive it.
]
---
count: false
# A few words 🤓 <hbr>
#### `\(\bullet\)` Mathematics is the foundation of everything.
#### `\(\bullet\)` It's the universal language and valuable asset.
#### `\(\bullet\)` Mastering it unlocks countless opportunities.
#### `\(\bullet\)` What's Math to you?
<hbr>
.pull-right-60[ <h1br>
# Some advices 🔑 <hbr>
#### `\(\bullet\)` Reevaluate how you perceive it.
#### `\(\bullet\)` Work hard, read more and practice more.
]
---
count: false
# A few words 🤓 <hbr>
#### `\(\bullet\)` Mathematics is the foundation of everything.
#### `\(\bullet\)` It's the universal language and valuable asset.
#### `\(\bullet\)` Mastering it unlocks countless opportunities.
#### `\(\bullet\)` What's Math to you?
<hbr>
.pull-right-60[ <h1br>
# Some advices 🔑 <hbr>
#### `\(\bullet\)` Reevaluate how you perceive it.
#### `\(\bullet\)` Work hard, read more and practice more.
#### `\(\bullet\)` Be curious, seek for the meaning behind it.
]
---
count: true
# A few words 🤓 <hbr>
#### `\(\bullet\)` Mathematics is the foundation of everything.
#### `\(\bullet\)` It's the universal language and valuable asset.
#### `\(\bullet\)` Mastering it unlocks countless opportunities.
#### `\(\bullet\)` What's Math to you?
<hbr>
.pull-right-60[ <h1br>
# Some advices 🔑 <hbr>
#### `\(\bullet\)` Reevaluate how you perceive it.
#### `\(\bullet\)` Work hard, read more and practice more.
#### `\(\bullet\)` Be curious, seek for the meaning behind it.
#### `\(\bullet\)` Make mistakes until you know it's a mistake.
]
---
# 📱You like scrolling facebook?
.center[
[<img src="img/fpb.jpeg" align="middle" height="200">](https://www.facebook.com/fpbcambodia)
[<img src="img/mac.png" align="middle" height="200">](https://www.facebook.com/wemaccommunity)
[<img src="img/AXK_logo_clear.png" align="middle" height="200">](https://www.facebook.com/AXKhmer)
]
Slide here: [https://hassothea.github.io/MLcourses/RUPP_Math_in_ML.html](https://hassothea.github.io/MLcourses/RUPP_Math_in_ML.html)
---
exclude:true
count: false
template: inter-slide
class: left, middle
count: false
.center[# References]<hbr>
&#128218; [Linder, T. (2002). Learning-Theoretic Methods in Vector Quantization. In: Györfi, L. (eds) Principles of Nonparametric Learning. International Centre for Mechanical Sciences, vol 434. Springer, Vienna.](https://link.springer.com/chapter/10.1007/978-3-7091-2568-7_4)
&#128218; [Banerjee, S. Merugu, I.S. Dhillon, and J. Ghosh. Clustering with Bregman divergences. Journal of Machine Learning Research, 6:1705–1749, 2005](chrome-extension://oemmndcbldboiebfnladdacbdfmadadm/https://jmlr.org/papers/volume6/banerjee05b/banerjee05b.pdf)
&#128218; [A. Fischer. Quantization and clustering with Bregman divergences. Journal of Multivariate Analysis, 101(10):2207–2221, 2010.](chrome-extension://oemmndcbldboiebfnladdacbdfmadadm/https://www.lpsm.paris/_media/users/fischer/quantization_and_clustering_with_bregman_divergences.pdf)
&#128218; [S. Has, A. Fischer, and M. Mougeot. Kfc: A clusterwise supervised learning procedure based on the aggregation of distances. Journal of Statistical Computation and Simulation, 0(0):1–21, 2021. doi: 10.1080/00949655.2021. 1891539.](https://www.tandfonline.com/doi/abs/10.1080/00949655.2021.1891539)
<svg aria-hidden="true" role="img" viewBox="0 0 496 512" style="height:1em;width:0.97em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg> [https://github.com/hassothea/KFC-Procedure](https://github.com/hassothea/KFC-Procedure)
<h0br>
.pull-right[
# Thank you 🤓
]
</textarea>
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