Deep learning allows computational models with several processing layers to learn multiple degrees of abstraction for data representations. These techniques have vastly enhanced the state-of-the-art in speech recognition, visual object recognition, object detection, and a variety of other fields like drug development and genomics. Deep learning uses the backpropagation algorithm to show how a machine should adjust its internal parameters that are used to compute the representation in each layer from the representation in the previous layer, revealing intricate structure in massive data sets.
Deep convolutional nets have revolutionised image, video, voice, and audio processing, while recurrent nets have shed light on sequential data like text and speech. Multiple processing layers are used in these deep-learning technologies, such as deep artificial neural networks, to identify patterns and structure in very large data sets.
Each layer learns a notion from the input, which is then built upon by successive layers; the higher the level, the more abstract the concepts learnt.Deep learning, a machine learning technique based on artificial neural networks, has emerged in recent years as a powerful tool for machine learning, with the potential to transform the future of AI. In addition to its predictive capability and capacity to create automatically optimal high-level features and semantic interpretation from the input data, rapid increases in processing power, fast data storage, and parallelization have all led to the technology’s rapid adoption.