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Understanding Neural Networks
We Explore How Neural Networks Function in Order to Build an Intuitive Understanding of Deep Learning
Deep learning is a hot topic these days. But what is it that makes it special and sets it apart from other aspects of machine learning? That is a deep question (pardon the pun). To even begin to answer it, we will need to learn the basics of neural networks.
Neural networks are the workhorses of deep learning. And while they may look like black boxes, deep down (sorry, I will stop the terrible puns) they are trying to accomplish the same thing as any other model — to make good predictions.
In this post, we will explore the ins and outs of a simple neural network. And by the end, hopefully you (and I) will have gained a deeper and more intuitive understanding of how neural networks do what they do.
The 30,000 Feet View
Let’s start with a really high level overview so we know what we are working with. Neural networks are multi-layer networks of neurons (the blue and magenta nodes in the chart below) that we use to classify things, make predictions, etc. Below is the diagram of a simple neural network with five inputs, 5 outputs, and two hidden layers of neurons.
Starting from the left, we have:
- The input layer of our model in orange.
- Our first hidden layer of neurons in blue.
- Our second hidden layer of neurons in magenta.
- The output layer (a.k.a. the prediction) of our model in green.
The arrows that connect the dots shows how all the neurons are interconnected and how data travels from the input layer all the way through to the output layer.
Later we will calculate step by step each output value. We will also watch how the neural network learns from its mistake using a process known as backpropagation.
