Part 4 of our tutorial series on Simple Neural Networks. We’re ready to write our Python script! Having gone through the maths, vectorisation and activation functions, we’re now ready to put it all together and write it up. By the end of this tutorial, you will have a working NN in Python, using only numpy, which can be used to learn the output of logic gates (e.g. XOR)
The third in our series of tutorials on Simple Neural Networks. This time, we’re looking a bit deeper into the maths, specifically focusing on vectorisation. This is an important step before we can translate our maths in a functioning script in Python.
This is the first part of a series of tutorials on Simple Neural Networks (NN). Tutorials on neural networks (NN) can be found all over the internet. Though many of them are the same, each is written (or recorded) slightly differently. This means that I always feel like I learn something new or get a better understanding of things with every tutorial I see. I’d like to make this tutorial as clear as I can, so sometimes the maths may be simplistic, but hopefully it’ll give you a good unserstanding of what’s going on. Please let me know if any of the notation is incorrect or there are any mistakes - either comment or use the contact page on the left.