What neural networks are and how they work
Neural networks and the modern technology landscape
Modern technology is bound up with neural networks. But what are they, and how do they work? Let's find out.
What is a neural network?
Neural networks are machine learning models inspired by the structure of the human brain. They are made up of nodes, or "neurons", organised into layers. Each neuron receives input, processes it and passes it on. Neural networks can handle large volumes of information and recognise complex patterns, which makes them an ideal fit for machine learning problems.
How is a neural network built?
The main components are the input layer, the hidden layers and the output layer. The input layer takes the initial data, the hidden layers process it, and the output layer produces the result. Neurons in the layers are connected by weights, which change during training so that the network adapts to the input.
The training process
During training a neural network learns to recognise patterns in the data. It adapts by changing the weights of the connections between neurons, minimising the gap between the predicted value and the true one. This process is called backpropagation, and it is central to how neural networks learn.
Types of neural network
There are many kinds — convolutional networks for image processing, recurrent networks for analysing sequences, and more. Which one you choose depends on the problem and the results you need.
In closing
Neural networks are a powerful tool in artificial intelligence and machine learning. They can learn, adapt and recognise complex structures in data, which opens up enormous possibilities for technology and science. Go deeper by studying further material and practising on neural network models of your own.
