DEEP LEARNING IN A NUTSHELL Deep learning, also known as hierarchical learning or deep structured learning, is a type of machine learning that uses a layered algorithmic architecture to analyze data.
In deep learning models, data is filtered through a cascade of multiple layers, with each successive layer using the output from the previous one to inform its results.
Deep learning models can become more and more accurate as they process more data, essentially learning from previous results to refine their ability to make correlations and connections.
Deep learning is loosely based on the way biological neurons connect with one another to process information in the brains of animals.
Similar to the way electrical signals travel across the cells of living creates, each subsequent layer of nodes is activated when it receives stimuli from its neighboring neurons.
In artificial neural networks (ANNs), the basis for deep learning models, each layer may be assigned a specific portion of a transformation task, and data might traverse the layers multiple times to refine and optimize the ultimate output.
These “hidden” layers serve to perform the mathematical translation tasks that turn raw input into meaningful output.
“Deep learning methods are representation-learning methods with multiple levels of representation, obtained by composing simple but non-linear modules that each transform the representation at one level (starting with the raw input) into a representation at a higher, slightly more abstract level,” explains a 2015 article published in Nature, authored by engineers from Facebook, Google, the University of Toronto, and Université de Montréal.
“With the composition of enough such transformations, very complex functions can be learned.
Higher layers of representation amplify aspects of the input that are important for discrimination and suppress irrelevant variations.
” This multi-layered strategy allows deep learning models to complete classification tasks such as identifying subtle abnormalities in medical images, clustering patients with similar characteristics into risk-based cohorts, or highlight relationships between symptoms and outcomes within vast quantities of unstructured data.
Unlike other types of machine learning, deep learning has the added benefit of being able to decisions with significantly less involvement from human trainers.
While basic machine learning requires a programmer to identify whether a conclusion is correct or not, deep learning can gauge the accuracy of its answers on its own due to the nature of its multi-layered structure.
Go to the full article Share this:Click to share on Twitter (Opens in new window)Click to share on Facebook (Opens in new window)Click to share on Google+ (Opens in new window)MoreClick to share on LinkedIn (Opens in new window)Click to share on Reddit (Opens in new window).. More details