Added Chapters 12 and 13

Chapters/12-Transformers/INDEX.md

@@ -21,8 +21,8 @@ to the first `encoder` and `decoder`.
 Here we transform each word of the input into an ***embedding*** and add a vector to account for
 position. This positional encoding can either be learnt or can follow this formula:
 
-
 - Even size:
+
 $$
 \text{positional\_encoding}_{
     (position, 2\text{size})
@@ -40,7 +40,9 @@ $$
         }
     \right)
 $$
+
 - Odd size:
+
 $$
 \text{positional\_encoding}_{
     (position, 2\text{size} + 1)
@@ -59,7 +61,6 @@ $$
     \right)
 $$
 
-
 ### Encoder
 
 > [!CAUTION]
@@ -164,17 +165,17 @@ It can be used as a classifier and can be fine tuned.
 The fine tuning happens by **masking** input and **predict** the **masked word**:
 
 - 15% of total words in input are masked
-    - 80% will become a `[masked]` token
-    - 10% will become random words
-    - 10% will remain unchanged
+  - 80% will become a `[masked]` token
+  - 10% will become random words
+  - 10% will remain unchanged
 
 #### Bert tasks
 
 - **Classification**
 - **Fine Tuning**
 - **2 sentences tasks**
-    - **Are they paraphrases?**
-    - **Does one sentence follow from this other one?**
+  - **Are they paraphrases?**
+  - **Does one sentence follow from this other one?**
 - **Feature Extraction**: "Allows us to extract feature to use in our model
 
 ### GPT-2