added figs and updated lectures

doc/src/week40/week40.do.txt

@@ -13,10 +13,15 @@ Reading suggestions for both days: "Aurelien Geron's chapter 10":"https://github
 For Stochastic Gradient Descent, we recommend chapter 4 of Geron's text.
 For neural networks we recommend Goodfellow et al chapters 6 and 7 and Bishop 5.1-5.4
 
-!split
-===== Overview video for week 40 =====
 
-"Overview Video, from Stochastic Gradient methods to Neural Networks":"https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h20/forelesningsvideoer/OverviewWeek40.mp4?vrtx=view-as-webpage"
+
+!split
+===== Overview video on Stochastic Gradient Descent =====
+
+"What is Stochastic Gradient Descent":"https://www.youtube.com/watch?v=vMh0zPT0tLI&ab_channel=StatQuestwithJoshStarmer"
+
+
+
 
 !split
 ===== Stochastic Gradient Descent =====
@@ -859,8 +864,14 @@ a /=b
 
 
 
+!split
+===== Videos on Neural Networks =====
 
 
+"Neural Networks demystified":"https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs"
+
+"Building Neural Networks from scratch":"https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex"
+
 
 
 
@@ -1043,6 +1054,12 @@ the hidden layer, the output nodes are always assumed to be linear, so
 as to not restrict the range of output values.
 
 
+!split
+=====  Illustration of a single perceptropn model and a multi-perceptron model =====
+
+FIGURE: [figures/nns.png, width=600 frac=0.8]  In a) we show a single perceptron model while in b) we dispay a network with two  hidden layers, an input layer and an output layer.
+
+
 !split
 ===== Mathematical model  =====