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12 changes: 6 additions & 6 deletions ...2_dl_basics/02_01_optimization/index.html → chapters/02_dl_basics/02_01_rnn/index.html
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<link rel="stylesheet" type="text/css" href="/dl4nlp/css/style.css">


<title>Deep Learning for Natural Language Processing (DL4NLP) | Chapter 02.01: Optimization</title>
<title>Deep Learning for Natural Language Processing (DL4NLP) | Chapter 02.01: Recurrent Neural Networks</title>


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</nav>
</div><div id="content" class="container">
<h1>Chapter 02.01: Optimization</h1>
<p>This chapter discusses optimization and the optimization algorithm gradient descent. The variants of stochastic gradient descent are commonly used for deep learning.</p>
<h1>Chapter 02.01: Recurrent Neural Networks</h1>
<p>This chapter introduces Recurrent Neural Networks in the context of Language Modelling and discusses different types of RNNs, such as LSTMs and Bidirectional RNNs.</p>
<h3 id="lecture-slides">Lecture slides</h3>


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<a href="https://github.com/slds-lmu/lecture_dl4nlp/raw/main/slides/chapter2-deeplearningbasics/slides-21-optimization.pdf">
<a href="https://github.com/slds-lmu/lecture_dl4nlp/raw/main/slides/chapter02-deeplearningbasics/slides-21-rnn.pdf">
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<ul class="section_skipper list-unstyled">


<li id="prev_in_section"><a class="btn btn-primary" href="https://slds-lmu.github.io/dl4nlp/chapters/02_dl_basics/02_02_dnns_activations/">Chapter 02.02 Deep Feedforward Networks &#xbb;</a></li>
<li id="prev_in_section"><a class="btn btn-primary" href="https://slds-lmu.github.io/dl4nlp/chapters/02_dl_basics/02_02_attention/">Chapter 02.02 Attention &#xbb;</a></li>

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<link rel="stylesheet" type="text/css" href="/dl4nlp/css/style.css">


<title>Deep Learning for Natural Language Processing (DL4NLP) | Chapter 02.02 Deep Feedforward Networks</title>
<title>Deep Learning for Natural Language Processing (DL4NLP) | Chapter 02.02 Attention</title>


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</nav>
</div><div id="content" class="container">
<h1>Chapter 02.02 Deep Feedforward Networks</h1>
<p>This chapter discusses Deep Forward Networks, the simplest type of neural networks and some common activation functions, also called non-linearities.</p>
<h1>Chapter 02.02 Attention</h1>
<p>This chapter provides a first introduction to the Attention mechanism as a way to model long range dependencies.</p>
<h3 id="lecture-slides">Lecture slides</h3>


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<a href="https://github.com/slds-lmu/lecture_dl4nlp/raw/main/slides/chapter2-deeplearningbasics/slides-22-dnns_activations.pdf">
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<ul class="section_skipper list-unstyled">

<li id="next_in_section"><a class="btn btn-primary" href="https://slds-lmu.github.io/dl4nlp/chapters/02_dl_basics/02_01_optimization/">&#xab; Chapter 02.01: Optimization</a></li>
<li id="next_in_section"><a class="btn btn-primary" href="https://slds-lmu.github.io/dl4nlp/chapters/02_dl_basics/02_01_rnn/">&#xab; Chapter 02.01: Recurrent Neural Networks</a></li>


<li id="prev_in_section"><a class="btn btn-primary" href="https://slds-lmu.github.io/dl4nlp/chapters/02_dl_basics/02_03_backpropagation/">Chapter 02.03: Backpropagation &#xbb;</a></li>
<li id="prev_in_section"><a class="btn btn-primary" href="https://slds-lmu.github.io/dl4nlp/chapters/02_dl_basics/02_03_elmo/">Chapter 02.03: ELMo &#xbb;</a></li>

</ul>

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<title>Deep Learning for Natural Language Processing (DL4NLP) | Chapter 02.03: Backpropagation</title>
<title>Deep Learning for Natural Language Processing (DL4NLP) | Chapter 02.03: ELMo</title>


<link rel="apple-touch-icon" sizes="180x180" href="/dl4nlp/apple-touch-icon.png">
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</nav>
</div><div id="content" class="container">
<h1>Chapter 02.03: Backpropagation</h1>
<p>The ability to scale neural networks is a key criterion for the success of modern neural networks. The backpropagation algorithm powers the efficient computation of the gradients.</p>
<h1>Chapter 02.03: ELMo</h1>
<p>In this chapter we introduce ELMo, a modelling approach, that enables us to contextualize word embeddings.</p>
<h3 id="lecture-slides">Lecture slides</h3>


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<a href="https://github.com/slds-lmu/lecture_dl4nlp/raw/main/slides/chapter2-deeplearningbasics/slides-23-backpropagation.pdf">
<a href="https://github.com/slds-lmu/lecture_dl4nlp/raw/main/slides/chapter02-deeplearningbasics/slides-23-elmo.pdf">
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<ul class="section_skipper list-unstyled">

<li id="next_in_section"><a class="btn btn-primary" href="https://slds-lmu.github.io/dl4nlp/chapters/02_dl_basics/02_02_dnns_activations/">&#xab; Chapter 02.02 Deep Feedforward Networks</a></li>
<li id="next_in_section"><a class="btn btn-primary" href="https://slds-lmu.github.io/dl4nlp/chapters/02_dl_basics/02_02_attention/">&#xab; Chapter 02.02 Attention</a></li>


<li id="prev_in_section"><a class="btn btn-primary" href="https://slds-lmu.github.io/dl4nlp/chapters/02_dl_basics/02_regularization/">Chapter 02.04 Regularization &#xbb;</a></li>
<li id="prev_in_section"><a class="btn btn-primary" href="https://slds-lmu.github.io/dl4nlp/chapters/02_dl_basics/02_04_tokenization/">Chapter 02.04 Revisiting words: Tokenization &#xbb;</a></li>

</ul>

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<title>Deep Learning for Natural Language Processing (DL4NLP) | Chapter 02.04 Regularization</title>
<title>Deep Learning for Natural Language Processing (DL4NLP) | Chapter 02.04 Revisiting words: Tokenization</title>


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</nav>
</div><div id="content" class="container">
<h1>Chapter 02.04 Regularization</h1>
<p>Regularization provides the possibility to guide the learning process.</p>
<h1>Chapter 02.04 Revisiting words: Tokenization</h1>
<p>In order to feed text data into a model we have to tokenize it first. This chapter discusses various types of text tokenization.</p>
<h3 id="lecture-slides">Lecture slides</h3>


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<a href="https://github.com/slds-lmu/lecture_dl4nlp/raw/main/slides/chapter2-deeplearningbasics/slides-24-regularization.pdf">
<a href="https://github.com/slds-lmu/lecture_dl4nlp/raw/main/slides/chapter02-deeplearningbasics/slides-24-tokenization.pdf">
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<li id="next_in_section"><a class="btn btn-primary" href="https://slds-lmu.github.io/dl4nlp/chapters/02_dl_basics/02_03_backpropagation/">&#xab; Chapter 02.03: Backpropagation</a></li>
<li id="next_in_section"><a class="btn btn-primary" href="https://slds-lmu.github.io/dl4nlp/chapters/02_dl_basics/02_03_elmo/">&#xab; Chapter 02.03: ELMo</a></li>


</ul>
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</div><div id="content" class="container">
<h1>Chapter 2: Deep Learning Basics</h1>

<p><p>This chapter gives a quick introduction to the basic concepts of deep learning, such as optimization, simple Feedforward networks, backpropagation and regularization. A comprehensive introduction is given in [1].</p>
<h3 id="references">References</h3>
<ul>
<li>[1] <a href="https://www.deeplearningbook.org/">Goodfellow et al., 2016</a></li>
</ul></p>
<p><p>This chapter gives a quick introduction to the basic concepts of deep learning in the context of NLP, such as RNN, attention, ELMo and tokenization.</p></p>


<div class="chapter_overview">
<ul class="list-unstyled">


<li>
<a class="title" href="/dl4nlp/chapters/02_dl_basics/02_01_optimization/">Chapter 02.01: Optimization</a>
<a class="title" href="/dl4nlp/chapters/02_dl_basics/02_01_rnn/">Chapter 02.01: Recurrent Neural Networks</a>


<p>This chapter discusses optimization and the optimization algorithm gradient descent. The variants of stochastic gradient descent are commonly used for deep learning.
<p>This chapter introduces Recurrent Neural Networks in the context of Language Modelling and discusses different types of RNNs, such as LSTMs and Bidirectional RNNs.
</p>


</li>

<li>
<a class="title" href="/dl4nlp/chapters/02_dl_basics/02_02_dnns_activations/">Chapter 02.02 Deep Feedforward Networks</a>
<a class="title" href="/dl4nlp/chapters/02_dl_basics/02_02_attention/">Chapter 02.02 Attention</a>


<p>This chapter discusses Deep Forward Networks, the simplest type of neural networks and some common activation functions, also called non-linearities.
<p>This chapter provides a first introduction to the Attention mechanism as a way to model long range dependencies.
</p>


</li>

<li>
<a class="title" href="/dl4nlp/chapters/02_dl_basics/02_03_backpropagation/">Chapter 02.03: Backpropagation</a>
<a class="title" href="/dl4nlp/chapters/02_dl_basics/02_03_elmo/">Chapter 02.03: ELMo</a>


<p>The ability to scale neural networks is a key criterion for the success of modern neural networks. The backpropagation algorithm powers the efficient computation of the gradients.
<p>In this chapter we introduce ELMo, a modelling approach, that enables us to contextualize word embeddings.
</p>


</li>

<li>
<a class="title" href="/dl4nlp/chapters/02_dl_basics/02_regularization/">Chapter 02.04 Regularization</a>
<a class="title" href="/dl4nlp/chapters/02_dl_basics/02_04_tokenization/">Chapter 02.04 Revisiting words: Tokenization</a>


<p>Regularization provides the possibility to guide the learning process.
<p>In order to feed text data into a model we have to tokenize it first. This chapter discusses various types of text tokenization.
</p>


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Expand Up @@ -90,7 +90,7 @@ <h1>Chapters</h1>
<a class="title" href="/dl4nlp/chapters/02_dl_basics/">Chapter 2: Deep Learning Basics</a>


<p>This chapter gives a quick introduction to the basic concepts of deep learning, such as optimization, simple Feedforward networks, backpropagation and regularization. A comprehensive introduction is given in [1].
<p>This chapter gives a quick introduction to the basic concepts of deep learning in the context of NLP, such as RNN, attention, ELMo and tokenization.
</p>


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Expand Up @@ -128,13 +128,13 @@ <h1>Deep Learning for NLP (DL4NLP)</h1>

<ul>

<li><a class="title" href="/dl4nlp/chapters/02_dl_basics/02_01_optimization/">Chapter 02.01: Optimization</a></li>
<li><a class="title" href="/dl4nlp/chapters/02_dl_basics/02_01_rnn/">Chapter 02.01: Recurrent Neural Networks</a></li>

<li><a class="title" href="/dl4nlp/chapters/02_dl_basics/02_02_dnns_activations/">Chapter 02.02 Deep Feedforward Networks</a></li>
<li><a class="title" href="/dl4nlp/chapters/02_dl_basics/02_02_attention/">Chapter 02.02 Attention</a></li>

<li><a class="title" href="/dl4nlp/chapters/02_dl_basics/02_03_backpropagation/">Chapter 02.03: Backpropagation</a></li>
<li><a class="title" href="/dl4nlp/chapters/02_dl_basics/02_03_elmo/">Chapter 02.03: ELMo</a></li>

<li><a class="title" href="/dl4nlp/chapters/02_dl_basics/02_regularization/">Chapter 02.04 Regularization</a></li>
<li><a class="title" href="/dl4nlp/chapters/02_dl_basics/02_04_tokenization/">Chapter 02.04 Revisiting words: Tokenization</a></li>

</ul>

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