What’s The Difference Between Neural Networks and Deep Learning
Technology and data have grown at an exponential rate over recent years. The likes of AI has seen a huge boost in new advancements. We have even created robots and AI that can not only adapt but continually learn in order for them to perform their tasks better.
But not all methods of learning and attaining data are the same – there are 2 main ways that computers can learn for themselves.
Neural networks aim to mimic how the human brain carries out the process of thinking. Our brains are organised into hierarchical tiers whereby all of the information we deal with is parsed through each ‘layer’ before being sent to the next level up the chain. For example, you will go through the following sequence of the thought process for most of the information you take in on your day to day life:
- Data input
- Decision Making
Whilst computers can’t replicate the complexity of this automated process just yet, they are getting there and it’s neural networks that are providing the basis of this development. In it’s simplest form, a neural network can have 3 ‘layers’: one for input, a hidden layer for data processing and the data output. Of course, they can get much more complex by adding additional hidden layers to process the information in different ways but they are limited to what they can do.
Deep learning is the next step above neural networks. Deep learning is capable of learning how to teach itself what to do with new information and how to process it, whereas neural networks require to be taught how to process new data. Computers achieve this ability to be self-taught through multiple layers created within a neural network because without such a network deep learning would not be possible; in order to act like a human brain, it must first replicate its structure. Deep learning passes information through many hidden processing layers (called a deep neural network) and ‘learns’ by filtering the information out to achieve a goal. This information is then shared between the neural network and used for future reference which results in the learning.
So whilst you might have first thought the two were interchangeable, in fact one relies on the other; without neural networks, there wouldn’t be deep learning.
The future of artificial intelligence and machine learning is going to continue to develop at incredible speeds over the next few years.