DevLog D-09toD-11. I can't Imagine the backprop.

Don't Learn here. I think Everything here is exact opposite of what you do in a real job. I am not a profession AI guy. I am a noob.

Forward pass was simple.
I need to pass the input value of one layer to another layers.

But Backpropagation has multiple steps.
I think backpropagation is when you make your network aware of loss. By Aware I mean updating value based on loss of network. I just realized this today(17-03-2024). So Below is the methods that I wrote to train and get loss of network.

 def train(self,train_X,train_Y):
        # pass the train_X to Network to get loss of network or the error in prediction. 
        self.inputL_out = self.inputL.forward(train_X)
        for l in range(0,len(self.neuronsLayers)):
            if l == 0:
                self.neuronsLayers[l].forward(self.inputL_out)
            else:
                self.neuronsLayers[l].forward(self.neuronsLayers[l-1].f_out)
            if l == len(self.neuronsLayers) -1:
                self.outL_f_out = self.outL.forward(self.neuronsLayers[l].f_out) #self.outL_f_out is output of network.

        # now calculate the loss. 
        # I am calculating two type for losses. 1. loss on actual answer 2. Loss on wrong answer. 
        losses = []
        for i in range(0,self.out_dim):
            if train_Y != i:
                if abs(self.outL_f_out[i]) > 0.6:
                    losses.append(abs(self.outL_f_out[i])-0.6) #prediction for other digits other then actual answer should be less then 0.6
                else:
                    losses.append(0)
            else:
                losses.append(0.99 - abs(self.outL_f_out[train_Y])) #loss for actual answer. 

        print(losses)
        print(self.outL_f_out)

Until today I was stuck on derivative and how to update weights.
After wasting two days(2 hours per day) I realized I need to update weight based on loss.

But still how do I update weight & by how much.
And why my network in predicting negative probability.(wrong choice of activation function I think.)

I am weak at math