Implementing a flexible neural network

Implementing a flexible neural network

Implementing your own neural network can be hard, especially if you're like me, coming from a computer science background, math equations/syntax makes you dizzy.

Implementing your own neural network can be hard, especially if you're like me, coming from a computer science background, math equations/syntax makes you dizzy and you would understand things better using actual code.

Today I'll show you how easy it is to implement a flexible neural network and train it using the backpropagation algorithm. I'll be implementing this in Python using only NumPy as an external library.

After reading this post, you should understand the following:

  • How to feed forward inputs to a neural network.
  • Use the Backpropagation algorithm to train a neural network.
  • Use the neural network to solve a problem.

In this post, we'll use our neural network to solve a very simple problem: Binary AND.

The code source of the implementation is available here.

Background knowledge

In order to easily follow and understand this post, you'll need to know the following:

  • The basics of Python / OOP.
  • An idea of calculus (e.g. dot products, derivatives).
  • An idea of neural networks.
  • (Optional) How to work with NumPy.

Rest assured though, I'll try to explain everything I do/use here.

neural networks

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