How can I implement back-propagation for medium-sized neural networks? I've been wanting to make my own Neural Network in Python, in order to better understand how it works. I've been following this series of videos as a sort of guide, but it seems the backpropagation will get much more difficult when you use a larger network, which I plan to do. He doesn't really explain how to scale it to larger ones.
Currently, my network feeds forward, but I don't have much of an idea of where to start with backpropagation. My code is posted below, to show you where I'm currently at (I'm not asking for coding help, just for some pointers to good sources, and I figure knowing where I'm currently at might help):
import numpy

class NN:
    prediction = []
    def __init__(self,input_length):
        self.layers = []
        self.input_length = input_length
    def addLayer(self, layer):
        self.layers.append(layer)
        if len(self.layers) &gt;1:
            self.layers[len(self.layers)-1].setWeights(len(self.layers[len(self.layers)-2].neurons))
        else:
            self.layers[0].setWeights(self.input_length)
    def feedForward(self, inputs):
        _inputs = inputs
        for i in range(len(self.layers)):
            self.layers[i].process(_inputs)
            _inputs = self.layers[i].output
        self.prediction = _inputs

    def calculateErr(self, target):
        out = []
        for i in range(0,len(self.prediction)):
            out.append(  (self.prediction[i] - target[i]) ** 2  )
        return out
        

class Layer:

    neurons = []
    weights = []
    biases = []
    output = []
    
    def __init__(self,length,function):
        for i in range(0,length):
            self.neurons.append(Neuron(function))
            self.biases.append(numpy.random.randn())

    def setWeights(self, inlength):
        for i in range(0,inlength):
            self.weights.append([])
            for j in range(0, inlength):
                self.weights[i].append(numpy.random.randn())
    
    def process(self,inputs):
        for i in range(0, len(self.neurons)):
            self.output.append(self.neurons[i].run(inputs,self.weights[i], self.biases[i]))
    

class Neuron:
    output = 0
    def __init__(self, function):
        self.function = function
    def run(self, inputs, weights, bias):
        self.output = self.function(inputs,weights,bias)
        return self.output

def sigmoid(n):
    return 1/(1+numpy.exp(n))


def inputlayer_func(inputs,weights,bias):
    return inputs

def l2_func(inputs,weights,bias):
    out = 0
    
    for i in range(0,len(inputs)):
        out += weights[i] * inputs[i]
    out += bias
    
    return sigmoid(out)

NNet = NN(2)


l2 = Layer(1,l2_func)


NNet.addLayer(l2)
NNet.feedForward([2.0,1.0])
print(NNet.prediction)

So, is there any resource that explains how to implement the back-propagation algorithm step-by-step?
Backpropagation isn't too much more complicated, but understanding it well will require a bit of mathematics.
This tutorial is my go-to resource when students want more detail, because it includes fully worked through examples.
Chapter 18 of Russell &amp; Norvig's book includes pseudocode for this algorithm, as well as a derivation, but without good examples.