Merge pull request #900 from huggingface/add-videos-to-chapter-1

embed youtube videos within pages

Author: burtenshaw

chapters/en/chapter1/2.mdx

@@ -9,6 +9,8 @@ Before jumping into Transformer models, let's do a quick overview of what natura
 
 ## What is NLP?[[what-is-nlp]]
 
+<Youtube id="iNzlxWUAjd4" />
+
 NLP is a field of linguistics and machine learning focused on understanding everything related to human language. The aim of NLP tasks is not only to understand single words individually, but to be able to understand the context of those words.
 
 The following is a list of common NLP tasks, with some examples of each:

chapters/en/chapter1/5.mdx

@@ -1,5 +1,7 @@
 # How 🤗 Transformers solve tasks
 
+<Youtube id="zsfR7eY9Uho" />
+
 In [Transformers, what can they do?](/course/chapter1/3), you learned about natural language processing (NLP), speech and audio, computer vision tasks, and some important applications of them. This page will look closely at how models solve these tasks and explain what's happening under the hood. There are many ways to solve a given task, some models may implement certain techniques or even approach the task from a new angle, but for Transformer models, the general idea is the same. Owing to its flexible architecture, most models are a variant of an encoder, a decoder, or an encoder-decoder structure. 
 
 <Tip>

chapters/en/chapter1/8.mdx

@@ -5,6 +5,8 @@
     classNames="absolute z-10 right-0 top-0"
 />
 
+<Youtube id="Xp2w1_LKZN4" />
+
 So far, we've explored the transformer architecture in relation to a range of discrete tasks, like text classification or summarization. However, Large Language Models are most used for text generation, and this is what we'll explore in this chapter.
 
 In this page, we'll explore the core concepts behind LLM inference, providing a comprehensive understanding of how these models generate text and the key components involved in the inference process.