Artificial intelligence (AI) and Deep Learning are considered to be the most trusted technologies built to solve difficult problems that use massive data sets. Under the vast umbrella of artificial intelligence services, neural networks and deep learning are the enablers of data processing at a granular level for the effective extraction of insights and value.
Modeled in accordance with the human brain, a Neural Network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the functionality of a human brain. The human brain is a network of multiple neurons, the same as an Artificial Neural Network is of multiple perceptrons.
Input Layer: As the name suggests, this layer accepts all the initial data for the neural network.
Hidden Layer: Between the input and the output layer is a set of layers known as Hidden layers place where all the computation is done.
Output Layer: The inputs go through a series of transformations via the hidden layer which produces the result for given inputs.
Deep Learning is an advanced field of Machine Learning that is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled. It automates the calculations feature extraction, making sure that very minimal human effort is required.
So, how these technologies are related?
Artificial Intelligence is the algorithms of getting machines inputs and to analyze the behavior of humans.
Machine learning is a subset of Artificial Intelligence (AI) that focuses on getting machines inputs and collect data sets to make and predict decisions of the data.
Deep learning is a subset of Machine Learning algorithms that uses the concept of neural network layers to solve complicated problems.
Machine Learning and Deep Learning both are linked to each other having co-ordinated fields. Machine Learning and Deep learning aids Artificial Intelligence by providing a set of algorithms to solve inputs and analyze datasets and neural networks to solve data-driven problems