Deep learning is an artificial intelligence function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. biggest advantages of it is   its ability to execute feature engineering by itself. In this approach, an algorithm scans the data to identify features which correlate and then combine them to promote faster learning without being told to do so explicitly.

Deep learning is largely responsible for today’s growth in the use of AI. The technology has given computers extraordinary powers, such as the ability to recognize speech almost as good as a human being, a skill too tricky to code by hand. Deep learning has also transformed computer vision and dramatically improved machine translation. It is now being used to guide and enhance all sorts of key processes in medicine, finance, marketing—and beyond.

According to research from Gartner, up to 80% of a company’s data is unstructured because most of it exists in different formats such as texts, pictures, pdf files and more. Unstructured data is hard to analyze for most machine learning algorithms, which means it’s also going unutilized. That is where deep learning can help.

Deep learning algorithms can be trained using different data formats, and still derive insights that are relevant to the purpose of its training. For example, a deep learning algorithm can uncover any existing relations between pictures, social media chatter, industry analysis, weather forecast and more to predict future stock prices of a given company.

Deep learning has turned applications that previously required vision expertise into engineering challenges solvable by non-vision experts. Deep learning transfers the logical burden from an application developer, who develops and scripts a rules-based algorithm, to an engineer training the system. It also opens a new range of possibilities to solve applications that have never been attempted without a human inspector. In this way, deep learning makes machine vision easier to work with, while expanding the limits of what a computer and camera can accurately inspect.

In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound. Deep learning models can achieve state-of-the-art accuracy, sometimes exceeding human-level performance. Models are trained by using a large set of labeled data and neural network architectures that contain many layers.

It is gaining much popularity due to it’s supremacy in terms of accuracy when trained with huge amount of data. It’s estimated that trillions of dollars of impacts globally from deep learning over the coming years. The recent researches predict a 1% – 9% increase in revenues for companies that deploy deep learning effectively. However, know-how and infrastructure are key. Deep learning models need to be retrained regularly to stay relevant. They also need tweaking to improve gradually over time and respond to new parameters .