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Learn How To Build A Simple Neural Network In 9 Lines of Python Code

- Take the inputs from a training set example, adjust them by the weights, and pass them through a special formula to calculate the neuron's output.
- Calculate the error, which is the difference between the neuron's output and the desired output in the training set example.
- Depending on the direction of the error, adjust the weights slightly.
- Repeat this process 10, 000 times.





- We used the Sigmoid curve to calculate the output of the neuron.
- If the output is a large positive or negative number, it signifies the neuron was quite confident one way or another.
- From Diagram 4, we can see that at large numbers, the Sigmoid curve has a shallow gradient.
- If the neuron is confident that the existing weight is correct, it doesn't want to adjust it very much. Multiplying by the Sigmoid curve gradient achieves this.


- exp - the natural exponential
- array - creates a matrix
- dot - multiplies matrices
- random - gives us random numbers
