Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised.
Deep learning architectures such as deep neural networks, deep belief networks and recurrent neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, autonomous locomotion and board game programs, where they have produced results comparable to and in some cases superior to human experts. With massive amounts of computational power, machines can now recognize objects and translate speech in real time.
Deep learning models are vaguely inspired by information processing and communication patterns in biological nervous systems yet have various differences from the structural and functional properties of biological brains, which make them incompatible with neuroscience evidences.
Nevertheless, deep learning is one of the most talked-about topics in the domain of computer science and technology. The implications are huge and we should explore deep learning as an enhancement and enabler to human intelligence; not a replacement.