In this text, we’ll construct a neural network from scratch and use it to categorise handwritten digits.
Why reinvent the wheel/neural network, I hear you say? Can’t I just use my favourite machine learning framework and be done with it? Yes, there are a lot of off-the-shelf frameworks you can use to construct a neural network (Keras, PyTorch, and TensorFlow to call a couple of). The thing with using certainly one of these is that they make it easy for us to treat neural networks like black boxes.
This isn’t at all times a foul thing. Often we’d like this level of abstraction in order that we are able to get to work on the issue at hand, but we must always still strive to no less than have a basic understanding of what is happening under the hood if we’re to make use of neural networks in our work.
Constructing a neural network from scratch is, in my view, the very best strategy to foster a deep understanding of how they work.
By the tip of this text, you should have learned in regards to the feedforward and backpropagation algorithms, what an activation function is, what the difference between an epoch and a batch is, and methods to train a neural network. We’ll finish with an example by training a neural network to recognise handwritten digits.
All code utilized in this text is on the market here on GitHub [1].
Neural networks, or artificial neural networks, are a form of machine learning algorithm. They form the core of many deep learning and artificial intelligence systems like computer vision, forecasting and speech recognition.
The structure of artificial neural networks is usually in comparison with the structure of biological neural networks within the brain. I’d at all times urge caution not to attract an excessive amount of from this comparison. Sure, artificial neural networks look a bit like biological neural networks nevertheless it is kind of an enormous leap to start out comparing them to something as complex as a human brain.
A neural network is made up of several layers of neurons. Each layer of neurons is activated based on the activations within the previous layer, a set of weights connecting the previous…