Added new chapter 2

slds-lmu · Apr 9, 2024 · bff1f78 · bff1f78
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diff --git a/content/chapters/02_dl_basics/02_01_optimization.md b/content/chapters/02_dl_basics/02_01_optimization.md
diff --git a/content/chapters/02_dl_basics/02_01_rnn.md b/content/chapters/02_dl_basics/02_01_rnn.md
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+---
+title: "Chapter 02.01: Recurrent Neural Networks"
+weight: 2001
+
+---
+This chapter introduces Recurrent Neural Networks in the context of Language Modelling and discusses different types of RNNs, such as LSTMs and Bidirectional RNNs.
+
+<!--more-->
+
+### Lecture slides
+
+{{< pdfjs file="https://github.com/slds-lmu/lecture_dl4nlp/blob/main/slides/chapter02-deeplearningbasics/slides-21-rnn.pdf" >}}
+
diff --git a/content/chapters/02_dl_basics/02_02_attention.md b/content/chapters/02_dl_basics/02_02_attention.md
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+---
+title: "Chapter 02.02 Attention"
+weight: 2002
+---
+
+This chapter provides a first introduction to the Attention mechanism as a way to model long range dependencies.
+
+<!--more-->
+
+### Lecture slides
+
+{{< pdfjs file="https://github.com/slds-lmu/lecture_dl4nlp/blob/main/slides/chapter02-deeplearningbasics/slides-22-attention.pdf" >}}
diff --git a/content/chapters/02_dl_basics/02_02_dnns_activations.md b/content/chapters/02_dl_basics/02_02_dnns_activations.md
diff --git a/content/chapters/02_dl_basics/02_03_backpropagation.md b/content/chapters/02_dl_basics/02_03_backpropagation.md
diff --git a/content/chapters/02_dl_basics/02_03_elmo.md b/content/chapters/02_dl_basics/02_03_elmo.md
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+---
+title: "Chapter 02.03: ELMo"
+weight: 2003
+---
+In this chapter we introduce ELMo, a modelling approach, that enables us to contextualize word embeddings.
+
+<!--more-->
+
+### Lecture slides
+
+{{< pdfjs file="https://github.com/slds-lmu/lecture_dl4nlp/blob/main/slides/chapter02-deeplearningbasics/slides-23-elmo.pdf" >}}
+
diff --git a/content/chapters/02_dl_basics/02_04_tokenization.md b/content/chapters/02_dl_basics/02_04_tokenization.md
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+---
+title: "Chapter 02.04 Revisiting words: Tokenization"
+weight: 2004
+---
+In order to feed text data into a model we have to tokenize it first. This chapter discusses various types of text tokenization.
+
+<!--more-->
+
+### Lecture slides
+
+{{< pdfjs file="https://github.com/slds-lmu/lecture_dl4nlp/blob/main/slides/chapter02-deeplearningbasics/slides-24-tokenization.pdf" >}}
diff --git a/content/chapters/02_dl_basics/02_regularization.md b/content/chapters/02_dl_basics/02_regularization.md
diff --git a/content/chapters/02_dl_basics/_index.md b/content/chapters/02_dl_basics/_index.md
@@ -1,10 +1,6 @@
 ---
 title: "Chapter 2: Deep Learning Basics"
 ---
-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].
+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.
 
-<!--more-->
-
-### References 
-
-- [1] [Goodfellow et al., 2016](https://www.deeplearningbook.org/)
+<!--more-->