News: Recently, the global Artificial Neural Network (ANN) Market report was published. It is an information bank that delivers comprehensive information about the market ranging from the establishment to the predictable growth trend. As per the current report, ANN Market to Set Phenomenal Growth from 2021 to 2028.
- The concept behind an 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.
- 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.
- 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.
- 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.
- 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.