diff --git a/Javascript/p005-ReLU-Activation.js b/Javascript/p005-ReLU-Activation.js
index 8a2b87c..6aad1d5 100644
--- a/Javascript/p005-ReLU-Activation.js
+++ b/Javascript/p005-ReLU-Activation.js
@@ -3,7 +3,7 @@ Creates a dense layer of neurons with a ReLU activation function, and feeds forw
 Associated YT tutorial: https://www.youtu.be/gmjzbpSVY1A
 */
 
-const math = require("mathjs");
+//const math = require("mathjs");
 
 // Moved this code from spiral-data.js written by @vancegillies
 // Updated by @daniel-kukiela
@@ -47,7 +47,7 @@ class Layer_Dense {
 		this.weights = math.random([n_inputs, n_neurons], -1.0, 1.0);
 		this.biases = math.zeros(1, n_neurons);
 	}
-	
+
 	forward (inputs) {
 		var biasesmat = this.biases;
 		// Since only adding matrices elementwise is supported, you need to make the biases into a matrix and not a vector.
@@ -58,16 +58,16 @@ class Layer_Dense {
 
 class Activation_ReLU {
 	constructor () {}
-	
+
 	forward (inputs) {
 		this.output = math.matrix(inputs._data.map(layer => layer.map(i => i<0?0:i)));
 	}
 }
 
-var layer1 = new Layer_Dense(4, 5);
+var layer1 = new Layer_Dense(2, 5);
 var activation1 = new Activation_ReLU();
 
 layer1.forward(X);
 //console.log(layer1.output);
 activation1.forward(layer1.output);
-console.log(activation1.output);
\ No newline at end of file
+console.log(activation1.output);
diff --git a/Javascript/p006-Softmax-Activation.js b/Javascript/p006-Softmax-Activation.js
new file mode 100644
index 0000000..ea6fd88
--- /dev/null
+++ b/Javascript/p006-Softmax-Activation.js
@@ -0,0 +1,102 @@
+/* This is a javascript implementation of neural networks from scratch in python  series.
+*
+*The part 6 bits i.e Softmax activation struct is declared and defined after line 718
+*
+* Link to the series on youtube: https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3
+*/
+
+// const math = require("mathjs");
+
+// Moved this code from spiral-data.js written by @vancegillies
+// Updated by @daniel-kukiela
+function spiral_data(points, classes) {
+  // Using MathJs functions to make matrices with zeros but converting to arrays for simplicity
+  const X = math.zeros(points * classes, 2).toArray();
+  const y = math.zeros(points * classes, "dense").toArray();
+  let ix = 0;
+  for (let class_number = 0; class_number < classes; class_number++) {
+    let r = 0;
+    let t = class_number * 4;
+
+    while (r <= 1 && t <= (class_number + 1) * 4) {
+      // adding some randomness to t
+      const random_t = t + math.random(points) * 0.008;
+      // Was `* 0.2` but reduced so you can somewhat see the arms of spiral in visualization
+      // Fell free to change it back
+
+      // converting from polar to cartesian coordinates
+      X[ix][0] = r * math.sin(random_t * 2.5);
+      X[ix][1] = r * math.cos(random_t * 2.5);
+      y[ix] = class_number;
+
+      // the below two statements achieve linspace-like functionality
+      r += 1.0 / (points - 1);
+      t += 4.0 / (points - 1);
+
+      ix++; // increment index
+    }
+  }
+  // Returning as MathJs matrices, could be arrays, doesnt really matter
+  return [math.matrix(X), math.matrix(y)];
+}
+
+let [X, y] = spiral_data(100, 3);
+
+class Layer_Dense {
+  constructor (n_inputs, n_neurons){
+    this.weights = math.random([n_inputs, n_neurons], -1.0, 1.0);
+    this.biases = math.zeros(1, n_neurons);
+  }
+
+  forward (inputs) {
+    var biasesmat = this.biases;
+		// Since only adding matrices elementwise is supported, you need to make the biases into a matrix and not a vector.
+		for (var i=0; i<inputs.size()[0]-1;i++) {biasesmat=math.concat(biasesmat, this.biases, 0);}
+		this.output = math.add(math.multiply(inputs, this.weights), biasesmat);
+  }
+}
+
+class Activation_ReLU {
+  forward (inputs) {
+		this.output = math.matrix(inputs._data.map(layer => layer.map(i => i<0?0:i)));
+  }
+}
+
+class Activation_Softmax {
+  forward (inputs) {
+    let exp_values = new Array;
+
+    inputs.forEach ((input) => {
+      if (Array.isArray(input)) {
+        input.forEach ((element) => {
+          exp_values.push(math.exp(element));
+        });
+      }
+      exp_values.push(math.exp(input));
+    });
+
+    let norm_base = math.sum(exp_values);
+    let norm_values = new Array;
+
+    exp_values.forEach ((element) => {
+      norm_values.push(element / norm_base);
+    });
+
+    this.output = norm_values;
+  }
+}
+
+var dense1 = new Layer_Dense(2, 3);
+var activation1 = new Activation_ReLU();
+
+var dense2 = new Layer_Dense(3, 3);
+var activation2 = new Activation_Softmax();
+
+
+dense1.forward(X);
+activation1.forward(dense1.output);
+
+dense2.forward(activation1.output);
+activation2.forward(dense2.output);
+
+console.log(activation2.output);
diff --git a/Javascript/p007-Categorical-Cross-Entropy-Loss.js b/Javascript/p007-Categorical-Cross-Entropy-Loss.js
new file mode 100644
index 0000000..0437fcf
--- /dev/null
+++ b/Javascript/p007-Categorical-Cross-Entropy-Loss.js
@@ -0,0 +1,16 @@
+/*
+* Calculating the loss with Categorical Cross Entropy
+* Associated with YT NNFS tutorial: https://www.youtube.com/watch?v=dEXPMQXoiLc
+*/
+
+softmax_output = [0.7, 0.1, 0.2]
+target_output = [1, 0, 0]
+
+loss = -(math.log(softmax_output[0]) * target_output[0] +
+         math.log(softmax_output[1]) * target_output[1] +
+         math.log(softmax_output[2]) * target_output[2])
+
+console.log(loss)
+
+console.log(-math.log(0.7))
+console.log(-math.log(0.5))