Unraveling Deep Learning Algorithms With Limited Data

The study proposes an alternative repurposing technique for turning the weakness of deep neural networks into strengths. Deep learning has been an expanse of artificial intelligence, heavily researched by the data scientists in the past few areas. Experts are more curious about supplementing this technology in sectors where human skills perform mundane tasks. As it uses big data, which is garnered from various sources, makes patterns of this collected data and learn to perform a task without any supervision, it becomes data Hungry, which becomes a major challenge when the data is in scarcity. Apart from being hungry, two significant drawbacks presented with deep learning is the opacity and its shallowness.