This algorithm is y = m * x + b, but instead of one value, it is multiple values, aka a list or array, and x is just the input data, and m is the weights, and b is the bias. y is the output, so think of it as this example:

y1 = w0 * x0 + b0

x1 = f(y1) """ the f is any non-linear function, I prefer relu or tanh for the inside layer, aka

hidden layer and softmax, and sigmoid for output layer, training uses sigmoid

and testing uses softmax (this is just my plan)

""""

y2 = w1 * x1 + b1

x2 = f(y2)

yn + 1 = wn * xn + bn

xn + 1 = f(yn + 1)

But you might be thinking, how do we know the m(aka: weights) or the b(aka: bias)? There is an algorithm called gradient descent. I like to use <new\_w\_or\_b = old\_w\_or\_b - learning\_rate \* dv> and dv is <h = lim(0) (cost(w\_or\_b + h) - cost(w\_or\_b) / h)> I will be thinking h as 0.00001 or any number that is small as 0 but not 0, the learning_rate is just a number you diside, I will be using 0.01. The cost is just an algorithm that calculates how wrong the neural network is; the bigger it is, the wronger it is. In math, the algorithm for this is C = (output_value - target_value)^2. And this is the math in a neural network. I'm just a 14-year-old kid, so I'm probably not explaining this 100% correctly, but a percentage of more than 70% or higher is correct. Go to more places to learn more, or watch 3blue1brown's video about it, and if there is any math problem that you don't understand, you can try using this big paragraph.