The concept behind an Artificial Neural Networks (ANN) is to define inputs and outputs, feed pieces of inputs to computer programs that function like neurons and make inferences or calculations.
It then forwards those results to another layer of computer programs and so on, until a result is obtained.
As part of this neural network, a difference between intended output and input is computed at each layer and this difference is used to tune the parameters to each program.
This method is called back-propagation and is an essential component to the Neural Network.
Q How is Artificial Neural Networks (ANN) setting done ?
A
Instead of CPUs, Graphic Processing Units (GPU) which are good at performing massive parallel tasks can be used for setting up ANNs.
A few free ANN frameworks are TensorFlow, Keras, PyTorch and Theano.
These can be used for both normal Machine Learning tasks like classification or clustering and for Deep Learning/ANN tasks.
Q Why called Neural Network?
A
Neuron is the building block of the brain and it inspired computer scientists from the 1950s to make a computer perform tasks like a brain does.
It is not a simple problem and the clue to its complexity is in the brain structure.
Q Why ANN?
A Making an artificial brain
We need billions of artificial neurons if we were to build an artificial brain.
With the increase in computing power, mimicking billions of neurons is now possible.
Q How much is popularity of ANNs ?
A
Data Science, used interchangeably with Machine Learning, is the computer technology that uses data to detect patterns.
Hand-written digit recognition is a good example of machine learning.
However, in order for the computer to do this task, large amounts of sample data need to be manually labelled as examples of images of digits.
The ANN mentioned above with its backpropagation does exactly this.
This is why ANNs have become hugely popular in the past decade. This approach of using neural networks of many layers to automatically detect patterns and parameters is called Deep Learning
