Neural Networks vs Deep Learning

Difference Between Neural Networks and Deep Learning

With the huge transition in today’s technology, it takes more than just Big Data and Hadoop to transform businesses. The firms of today are moving towards AI and incorporating machine learning as their new technique. Neural networks or connectionist systems are the systems which are inspired by our biological neural network. These kinds of systems are trained to learn and adapt themselves according to the need. For example, in case of image recognition, once they are identified with cats, they can easily use that result set to separate images with cats with the ones with no cats. While doing this they do not have any prior knowledge about the characteristics of cat but they develop their own set of unique features which is helpful in their identification. Another term which is closely linked with this is deep learning also known as hierarchical learning. This is based upon learning data representations which are opposite to task-based algorithms. It can further be categorized into supervised, semi-supervised and unsupervised learning techniques. There are several architectures associated with Deep learning such as deep neural networks, belief networks and recurrent networks whose application lies with natural language processing, computer vision, speech recognition, social network filtering, audio recognition, bioinformatics, machine translation, drug design and the list goes on and on. Let us discuss Neural Networks and Deep Learning in detail in our post.

Head to Head comparison between Neural Networks vs Deep learning (Infographics)

Key differences between Neural Networks vs Deep learning:

The differences between Neural Networks and Deep learning are explained in the points presented below:

Neural networks make use of neurons that are used to transmit data in the form of input values and output values. They are used to transfer data by using networks or connections. Deep learning, on the other hand, is related to transformation and extraction of feature which attempts to establish a relationship between stimuli and associated neural responses present in the brain.
Application areas for neural networking includes system identification, natural resource management, process control, vehicle control, quantum chemistry, decision making, game playing, face identification, pattern recognition , signal classification, sequence recognition, object recognition, finance, medical diagnosis, visualization, data mining, machine translation, email spam filtering, social network filtering, etc. whereas application of deep learning includes Automatic speech recognition, Image recognition, visual art processing, Natural language processing, drug discovery and toxicology, customer relationship management, recommendation engines, Mobile advertising, bioinformatics, Image restoration etc.
Criticism encountered for Neural networks include those like training issues, theoretical issues, hardware issues, practical counterexamples to criticisms, hybrid approaches whereas for deep learning it is related with theory, errors, cyber threat, etc.