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books.html

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-Fairness and Machin Learning
+Fairness and Machine Learning
 </h5>
 <div class="card-text listing-description">
 Many take the leap of faith behind statistical decision making for granted to an extent that it’s become difficult to question. In this book, we take machine learning as a…
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+<a href="https://www.oreilly.com/library/view/hands-on-machine-learning/9781098125967/" class="quarto-grid-link">
+<div class="quarto-grid-item card h-100 card-left">
+<p class="card-img-top">
+<img loading="lazy" src="./books/images/handson-ml3.jpg" class="thumbnail-image card-img" style="height: 440px;">
+</p>
+<div class="card-body post-contents">
+<h5 class="no-anchor card-title listing-title">
+Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition
+</h5>
+<div class="card-text listing-description">
+Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this…
+</div>
+<div class="card-attribution card-text-small justify">
+<div class="listing-author">
+Aurélien Géron
+</div>
+<div class="listing-date">
+Oct 1, 2022
+</div>
+</div>
+</div>
+</div>
+</a>
+</div>
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 No matching items

books/fairml.html

@@ -12,7 +12,7 @@
 <meta name="dcterms.date" content="2023-01-01">
 <meta name="description" content="Many take the leap of faith behind statistical decision making for granted to an extent that it’s become difficult to question. In this book, we take machine learning as a reason to revisit this leap of faith and to interrogate how institutions make decisions about individuals. If machine learning is our way into studying institutional decision making, fairness is the moral lens through which we examine those decisions.">
 
-<title>Fairness and Machin Learning – Anthology of Data Science</title>
+<title>Fairness and Machine Learning – Anthology of Data Science</title>
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@@ -85,7 +85,7 @@
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@@ -441,7 +441,7 @@
 
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 <div class="quarto-title">
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+<h1 class="title">Fairness and Machine Learning</h1>
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cases/index.html

@@ -521,7 +521,7 @@ <h1 class="title">Selected use cases &amp; applications</h1>
 
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lectures.html

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notebooks/deconfounding/index.out.ipynb

@@ -20,7 +20,7 @@
         "And also we have 1000 $(X, Y)$ tuples as our data and that we want to\n",
         "build a regressions model."
       ],
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@@ -167,7 +167,7 @@
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