correct eq numbering, add impl of resent50

materialandmethods.typ

@@ -1,5 +1,6 @@
 #import "@preview/subpar:0.1.1"
 #import "utils.typ": todo
+#import "@preview/equate:0.2.1": equate
 
 = Material and Methods
 
@@ -283,17 +284,16 @@ The softmax function has high similarities with the Boltzmann distribution and w
 === Cross Entropy Loss
 #todo[Maybe remove this section]
 Cross Entropy Loss is a well established loss function in machine learning.
-@crel #cite(<crossentropy>) shows the formal general definition of the Cross Entropy Loss.
-And @crel is the special case of the general Cross Entropy Loss for binary classification tasks.
+@crelformal #cite(<crossentropy>) shows the formal general definition of the Cross Entropy Loss.
+And @crelbinary is the special case of the general Cross Entropy Loss for binary classification tasks.
 
 $
-H(p,q) &= -sum_(x in cal(X)) p(x) log q(x)\
-H(p,q) &= -(p log(q) + (1-p) log(1-q))\
-cal(L)(p,q) &= -1/N sum_(i=1)^(cal(B)) (p_i log(q_i) + (1-p_i) log(1-q_i))
+H(p,q) &= -sum_(x in cal(X)) p(x) log q(x) #<crelformal>\
+H(p,q) &= -(p log(q) + (1-p) log(1-q)) #<crelbinary>\
+cal(L)(p,q) &= -1/N sum_(i=1)^(cal(B)) (p_i log(q_i) + (1-p_i) log(1-q_i)) #<crelbatched>
 $ <crel>
-#todo[Check how multiline equation refs work]
 
-Equation~$cal(L)(p,q)$ @crel #cite(<handsonaiI>) is the Binary Cross Entropy Loss for a batch of size $cal(B)$ and used for model training in this Practical Work.
+Equation~$cal(L)(p,q)$ @crelbatched #cite(<handsonaiI>) is the Binary Cross Entropy Loss for a batch of size $cal(B)$ and used for model training in this Practical Work.
 
 === Cosine Similarity
 To measure the distance between two vectors some common distance measures are used.