If you want to get the most of Big Data, you should consider Deep Learning. With its ability to parse highly unstructured data and detect correlations and patterns, Deep Learning can bring a great value to various companies.
Deep Learning is a subsection of Machine Learning, which applies Artificial Neural Networks and algorithms in order to learn from extremely large amounts of data. Deep learning for business is able to deliver huge value for everyday tasks and routine work. By analyzing and categorizing corporate data, deep learning-powered systems can obtain valuable insights and make independent predictions without human intervention.
Deep learning solutions are increasingly used in most industries, among which are the manufacturing, retail, hospitality, energy, finance, marketing and other industries. You can easily find examples of Deep Learning use cases in various enterprises and organizations. The technology of deep learning is applied to speech recognition, image recognition, natural language processing (NLP), automatic drug detection and toxicology, CRM operations, data-based health predictions, and much more.
The most substantial advantages of Deep Learning
Saved Expenses And Time
As artificial neural networks operate the way human brains do and do not require extra manual training, Deep Learning applications can swiftly perform repetitive and human-consuming tasks.
Being error-proof, deep learning-based software works more accurately than human employees, avoiding common mistakes.
Deep Learning-driven software is more effective in processing different types of formats such as texts, pictures, videos, as well as sounds, hence paving the way to various innovative applications.
Some perks of using Deep Learning:
Utilization of Unstructured Data
Not all data are accurately structured to be trained on, conversely, they are mostly unstructured and exist in different format types. Traditional Machine Learning systems are not as effective as Deep Learning ones in the utilization of various data formats. Using neural networks allows Deep Learning to reveal any existing relations between various data types, for instance, social media chatters, company reports, and industry analysis.
No Need for Feature Engineering
Deep Learning is able to create new features on its own. Previously companies always needed a data scientist to perform feature engineering, the process of extracting features from raw data to better understand occurred problems. With the help of Deep Learning, software systems can effectively find patterns that correlate and combine them to enable faster learning without explicitly being programmed.
No Need for Data Labeling
Most innovative applications depend on labelled data. Data Labeling, the process of the manual curation of data, can be expensive and time-consuming. Applying Deep Learning, companies do not need to spend their resources on labelling data, as Deep Learning does this in a fully automated way.