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 and drug design,where they have produced results comparable to and in some cases superior to human experts.
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.
There are many applications of Deep learning in present days , some of them are listed below:
- Automatic Machine Translation.
- Object Classification in Photographs.
- Predictive typing
- Natural Language processing
- Audio processing to support voice based commands.
- Predicting machine / hardware failures and suggesting preventive maintenance
- Automatic Game Playing.
From Above Image one can easily understand that why deep learning is in trends nowdays.As Amount of data is increasing day by day , Deep learning algorithms are becoming more popular. Below image shows the difference between Deep learning and machine learning.