Visualization of a simple neural network for educational purposes.
This is implementation of neural network with back-propagation. There aren't any special tricks, it's as simple neural network as it gets.
The cost is defined as \(C = \frac{1}{2 \times sampleCnt}\sum^{sampleCnt}_{m=1}(\sum^{outputSize}_{n=1}(neruon_n-target_n)^2)\). In words: Error is defined as \((value - target)^2\). To get error of neural network for one training sample, you simply add errors of all output neurons. The total cost is then defined as average error of all training samples.
Let's say that the value of connection is the connection's weight (how wide it is) times the first connected neuron. To calculate the value of some neuron you add the values of all incoming connections and apply the sigmoidfunction to that sum. Other activation functions are possible, but I have not implemented them yet.
The cost function defined above is a function dependend on weights of connections in the same way as \(f(x, y) = x^2 + y^2\) is dependend on x and y. In the beginning, the weights are random. Let's say x = 5 and y = 3. The cost at this point would be 25 + 9 = 34, which we want to get to 0. Now we take the derivate with respect to each of these weights, which tells us how to adjust the wieghts to minimize the function. \(\frac{\partial f(x, y)}{\partial x} = 2x\), \(\frac{\partial f(x, y)}{\partial y} = 2y\). Now that we have the derivatives, we know the "direction" in which to change the weights. \(x_{new} = x_{old} - rate \times 2x = 5 - 0.1 \times 2 \times 5 = 4\) and that's a little bit closer to the desired 0 result of f(x, y). The rate is necessary to avoid stepping over the minimum.
In practice is the computation of the derivatives is a little bit harder, but all you need to know is the chain rule. I highly recommend 3blue1brown's series and this paper for better understanding.
If you have enjoyed this project, you might also like my tool for creating gif memes.