How do Artificial Neural Networks Work?
A typical Neural Network contains a large number of artificial neurons called units arranged in a series of layers. The input layer is where rules are predetermined and representative examples are given to show the ANN what the output should look like. Hidden layers are where the input is processed and “broken down”. These layers are shown in the figure 2.
Figure 2: Neural network
A typical Neural Network contains a large number of artificial neurons called units arranged in a series of layers. These layers are shown in the figure 2, above.
The layers can be explained as follows:
- Input layer – It contains those units (Artificial Neurons) which receive input from the outside world on which network will learn, recognise about or otherwise process.
- Output layer – It contains units that respond to the information about how it’s learned any task.
- Hidden layer – These units are in between input and output layers. The job of the hidden layer is to transform the input into something that output unit can use in some way.
Most Neural Networks are fully connected that means to say each hidden neuron is fully linked to every neuron in its previous layer (input) and to the next layer (output) layer.
Looking at an analogy may be helpful in understanding neural networks better. Learning in a neural network closely resembles to how we learn how to do things as humans— we perform an action and are either satisfied or dissatisfied with the result. Unsatisfactory results tend to cause a person to repeat a task until they succeed in achieving the desired result. Similarly, neural networks require a “supervisor” in order to describe what the desired result should be in response to the input.
Most deep learning methods use neural network architectures, which is why it is often referred to as deep neural networks. Deep learning models are trained by feeding them large amounts of labeled data and neural network architectures that learn features directly from the data without the need for manual input from a supervisor.
In deep-learning networks, each layer of nodes learns on a distinct set of features based on the previous layer’s output. The further you advance into the neural net, the more complex the features your nodes can recognise, since they aggregate and recombine features from the previous layer. This is known as feature hierarchy which is a hierarchy of increasing complexity with each layer.
Based on the difference between the actual value and the predicted value, an error value which is also called Cost Function is calculated and sent back through the system.