Deep Learning

Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost.

Deep learning attempts to mimic the human brain—albeit far from matching its ability—enabling systems to cluster data and make predictions with incredible accuracy.

Maximum utilization of unstructured data

Research from Gartner revealed that a huge percentage of an organization's data is unstructured because the majority of it exists in different types of formats like pictures, texts etc. For the majority of machine learning algorithms, it's difficult to analyze unstructured data, which means it's remaining unutilized and this is exactly where deep learning becomes useful.

Elimination of the need for feature engineering

In machine learning, feature engineering is a fundamental job as it improves accuracy and sometimes the process can require domain knowledge about a certain problem. One of the biggest advantages of using deep learning approach 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. This ability helps data scientists to save a significant amount of work.

Ability to deliver high-quality results

Humans get hungry or tired and sometimes make careless mistakes. When it comes to neural networks, this isn't the case. Once trained properly, a deep learning model becomes able to perform thousands of routine, repetitive tasks within a relatively shorter period of time compared to what it would take for a human being. In addition, the quality of the work never degrades, unless the training data contains raw data which doesn't represent the problem you're trying to solve.

Elimination of unnecessary costs

Recalls are highly expensive and for some industries, a recall can cost an organization millions of dollars in direct costs. With the help of deep learning, subjective defects which are hard to train like minor product labeling errors etc can be detected.

Elimination of the need for data labeling

Data labeling can be an expensive and time-consuming job. With a deep learning approach, the need for well-labeled data becomes obsolete as the algorithms excel at learning without any guideline. Other types of machine learning approaches aren't nearly as successful as this type of learning.