What is deep learning and tell its applications? Follow

Deep learning can be defined as an important aspect of artificial intelligence (AI) which deals with imitating of the learning approach where human beings get the scope to gain specific categories of knowledge. Deep learning is basically a part of the broader family of the neural network methods which is based on the neural convolutional networks.

Deep learning captures the working style of the human brain towards processing data and for the creation of patterns for application in decision making. Deep learning can be called a subset of machine learning in AI having networks which are empowered to learn unsupervised format of data which is unstructured. It is a technique which educates computers to act in a way which is natural to humans. Deep learning acts as one of the key technologies behind the functioning of driverless cars, which is supporting them to identify the stop sign, or to differentiate a pedestrian from the light-post. 

Deep learning is so advanced that it managed to show its impact in different spheres of life. The major application of deep learning includes:

1. Self-Driving Cars
2. News Aggregation and Fraud News Detection
3. Natural Language Processing (NLP)
4. Virtual Assistants
5. Entertainment (VEVO, Film Making, Netflix and Sports Highlights)
6. Visual Recognition
7. Fraud Detection
8. Healthcare 
9. Personalizations 
10. Detecting Developmental Delay in Children

How deep learning works?

Well, it consists of two major phases: one is training and the other one is inferring. The training phase can be defined as the process which deals with the labeling of huge amounts of data and for determining the matching characteristics. Here the system will make the comparison of the characteristics and will memorize them to deliver the right conclusions when it encounters a similar type of data in the future.

Training process:

1. ANNs (Artificial Neural Networks) will ask a set of binary false or true questions.
2. Will extract the numerical values from the block of data.
3. Classification of the data based on the answers which are received.
4. Labeling of the Data.

Inferring Phase:

Well in this phase the deep learning AI gives the conclusions and labels it as new unexposed data with application of their earlier knowledge.

Deep learning reduces the demand for future engineering and is an important architecture which one can adapt to fresh problems. This is all in short about deep learning and its applications.

Read more:  What companies are working on artificial intelligence and deep learning?