<p>Backpropagation isn't too much more complicated, but understanding it well will require a bit of mathematics.</p>
<p><a href="https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/" rel="nofollow noreferrer">This tutorial</a> is my go-to resource when students want more detail, because it includes fully worked through examples.</p>
<p>Chapter 18 of <a href="https://rads.stackoverflow.com/amzn/click/com/0136042597" rel="nofollow noreferrer" rel="nofollow noreferrer">Russell &amp; Norvig's</a> book includes pseudocode for this algorithm, as well as a derivation, but without good examples.</p>

CC BY-SA 4.0
2018-08-14T20:41:46.657Z
[Table of Contents for AI: A Modern Approach](http://aima.cs.berkeley.edu/contents.html)
DukeZhou
CC BY-SA 4.0
2018-08-13T15:05:05.983Z
2020-12-22T23:56:17.580Z
2020-12-22T23:56:17.580Z
John Doucette
<p>Nowadays, there are many resources that cover the back-propagation algorithm and some of them provide step-by-step examples.</p>
<p>However, in addition to <a href="https://ai.stackexchange.com/a/7545/2444">the other answer</a>, I would like to mention the online book <a href="http://neuralnetworksanddeeplearning.com/" rel="nofollow noreferrer">Neural Networks and Deep Learning</a> by Nielsen that covers <a href="http://neuralnetworksanddeeplearning.com/chap2.html" rel="nofollow noreferrer">the back-propagation algorithm</a> (and other topics) in detail and, at the same, intuitively, although some could disagree. You can find the associated source code <a href="https://github.com/mnielsen/neural-networks-and-deep-learning" rel="nofollow noreferrer">here</a> (which I had consulted a few years ago when I was learning about the topic).</p>

CC BY-SA 4.0
2020-12-23T00:10:20.207Z
2020-12-23T00:10:20.207Z
nbro
<p>I've been wanting to make my own Neural Network in Python, in order to better understand how it works. I've been following <a href="https://www.youtube.com/watch?v=ZzWaow1Rvho" rel="nofollow noreferrer">this</a> 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.</p>
<p>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):</p>
<pre><code>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)
</code></pre>
<p>So, is there any resource that explains how to implement the back-propagation algorithm step-by-step?</p>

<neural-networks><reference-request><backpropagation><implementation><resource-request>
How can I implement back-propagation for medium-sized neural networks?
Caleb H.
# How can I implement back-propagation for medium-sized neural networks?

(Asked by: username_0 on 2018-08-11)

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) >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?

**Tags:** neural-networks, reference-request, backpropagation, implementation, resource-request


---

## 2 answers

### Answer by username_1 on 2018-08-13. Score: 2 | Accepted answer

Backpropagation isn't too much more complicated, but understanding it well will require a bit of mathematics.

[This tutorial](https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/) is my go-to resource when students want more detail, because it includes fully worked through examples.

Chapter 18 of [Russell & Norvig's](https://rads.stackoverflow.com/amzn/click/com/0136042597) book includes pseudocode for this algorithm, as well as a derivation, but without good examples.

> **Comments:**
> **username_2**: [Table of Contents for AI: A Modern Approach](http://aima.cs.berkeley.edu/contents.html)  

---

### Answer by username_3 on 2020-12-23. Score: 0

Nowadays, there are many resources that cover the back-propagation algorithm and some of them provide step-by-step examples.

However, in addition to [the other answer](https://ai.stackexchange.com/a/7545/2444), I would like to mention the online book [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/) by Nielsen that covers [the back-propagation algorithm](http://neuralnetworksanddeeplearning.com/chap2.html) (and other topics) in detail and, at the same, intuitively, although some could disagree. You can find the associated source code [here](https://github.com/mnielsen/neural-networks-and-deep-learning) (which I had consulted a few years ago when I was learning about the topic).


---

