Deep learning is the subset of the machine learning that deals with the large number of datasets that human being uses to gain certain type of knowledge. It is the one of the trending technology. Deep learning allows machines to solve complex problems even it is relatively slow but exceptionally accurate. Deep learning requires large amount of labelled data for processing or to for perform the operations. It requires more deeper insights of data to train the model and can handle complex datasets as compared to machine learning.
Deep learning services includes learning of deep structured and unstructured representation of data to getting the solution from algorithms to solve machine learning problems. Deep learning algorithms also called the future of the predictive supply chain. In simple, the more deep learning algorithms learn, the better they perform.
For example, in face recognition if machine learning is checking parameters like nose, chin, lips etc. then deep learning will check out even skin tone at various parts of face, may try to figure out density of skin, eyeballs etc.
The term ‘deep’ refers to the number of hidden layers in the network and the term ‘learning’ here refers to the to learn about those hidden layers how they perform, how they work in the particular algorithm. To make any application successful it requires the huge amount of data to train the model so that our model gives the best and accurate result.
Various applications of deep learning are automatic speech recognition, Image recognition, visual art processing, natural language processing, Medical image analysis.
Deep learning includes multiple hidden layer for processing. In medical domain to detect the automatically cancer cells deep learning is used. At the industrial site, deep learning is used to improve worker safety around heavy machinery by automatically detecting when any person is near the danger zone.