text: **Deep learning**
  Deep learning[110] uses several layers of neurons between the network's 
  inputs and outputs. The multiple layers can progressively extract higher- 
  level features from the raw input. For example, in image processing, lower 
  layers may identify edges, while higher layers may identify the concepts 
  relevant to a human such as digits, letters, or faces.[112] 
  
  Deep learning has profoundly improved the performance of programs in 
  many important subfields of artificial intelligence, including computer 
  vision, 
  image 
  classification,[113] and others. The reason that deep learning performs so 
  well in so many applications is not known as of 2023.[114] The sudden success of deep learning in 2012– 
  2015 did not occur because of some new discovery or theoretical breakthrough (deep neural networks and 
  backpropagation had been described by many people, as far back as the 1950s)[i] but because of two 
  factors: the incredible increase in computer power (including the hundred-fold increase in speed by 
  switching to GPUs) and the availability of vast amounts of training data, especially the giant curated 
  datasets used for benchmark testing, such as ImageNet.[j] 
  
  speech  recognition, natural  language processing, 