Update Readme.md

Author: purvasingh96

Chapter-wise code/Code - PyTorch/6. Natural-Language-Processing/6. Machine Translation/NMT-Basic/Readme.md

@@ -14,31 +14,6 @@ Here, we have 3 given parameters:
 
 Now, we have to create an English embedding matrix and French embedding matrix:<br>
   <img src="./images/en_fr_embeddings.png"></img><br>
-```python
-# loop through all english, french word pairs in the english french dictionary
-    for en_word, fr_word in en_fr.items():
-
-        # check that the french word has an embedding and that the english word has an embedding
-        if fr_word in french_set and en_word in english_set:
-
-            # get the english embedding
-            en_vec = english_vecs[en_word]
-
-            # get the french embedding
-            fr_vec = french_vecs[fr_word]
-
-            # add the english embedding to the list
-            X_l.append(en_vec)
-
-            # add the french embedding to the list
-            Y_l.append(fr_vec)
-
-    # stack the vectors of X_l into a matrix X
-    X = np.vstack(X_l)
-
-    # stack the vectors of Y_l into a matrix Y
-    Y = np.vstack(Y_l)
-```
 
 ## 2. Linear Transformation of Word Embeddings
 Given dictionaries of English and French word embeddings we will create a transformation matrix `R`. In other words, given an english word embedding, `e`, we need to multiply `e` with `R`, i.e., (`eR`) to generate a new word embedding `f`.
@@ -51,6 +26,7 @@ Original Forbenius Norm: <img src="./images/original_forbenius_norm.png"></img><
 Modified Forbenius Norm: <img src="./images/modified_forbenius_norm.png"></img><br>
 Finally, our loss funtion will look something like this:<br>
 <img src="./images/final_loss_function.png"></img><br>
+<img src="./images/description.png"></img>