On this article, we’ll construct a neural community from scratch and use it to categorise handwritten digits.
Why reinvent the wheel/neural community, I hear you say? Can’t I simply use my favorite machine studying framework and be executed with it? Sure, there are various off-the-shelf frameworks that you should use to construct a neural community (Keras, PyTorch, and TensorFlow to call a number of). The factor with utilizing certainly one of these is that they make it simple for us to deal with neural networks like black bins.
This isn’t all the time a nasty factor. Usually we’d like this degree of abstraction in order that we will get to work on the issue at hand, however we must always nonetheless attempt to at the least have a fundamental understanding of what’s going on below the hood if we’re to make use of neural networks in our work.
Constructing a neural community from scratch is, in my view, one of the best ways to foster a deep understanding of how they work.
By the top of this text, you’ll have realized concerning the feedforward and backpropagation algorithms, what an activation perform is, what the distinction between an epoch and a batch is, and prepare a neural community. We are going to end with an instance by coaching a neural community to recognise handwritten digits.
All code used on this article is accessible right here on GitHub [1].
Neural networks, or synthetic neural networks, are a kind of machine studying algorithm. They type the core of many deep studying and synthetic intelligence techniques like laptop imaginative and prescient, forecasting and speech recognition.
The construction of synthetic neural networks is typically in comparison with the construction of organic neural networks within the mind. I’d all the time urge warning not to attract an excessive amount of from this comparability. Certain, synthetic neural networks look a bit like organic neural networks however it’s fairly an enormous leap to start out evaluating them to one thing as complicated as a human mind.
A neural community is made up of a number of layers of neurons. Every layer of neurons is activated primarily based on the activations within the earlier layer, a set of weights connecting the earlier…
