DEEP LEARNING

 



Deep learning, a buzz in the artificial intelligence field, is the subset of machine learning. It plays a major role from search engines to self driving cars that demand high computational power. The data is the “fuel” of deep learning. It’s a reality, as we have already said, and together with the great computing power, one of the reasons why automatic learning has gained relevance in recent years .What is meant by deep learning?..

 Deep learning is nothing but a subset of machine learning, which is essentially a neural network with three or more layers. These neural networks attempt to stimulate the behavior of human brain, allowing it to learn from large amounts of data. Deep learning drives many Artificial Intelligence applications that improve automation, performing analytical and physical tasks without human intervention.

Remember the word “accuracy”. Deep learning achieves recognition accuracy at higher levels than ever before. It has two main reasons for its achievement they are:

 

1.    1. Deep learning requires large amount of labeled data.

2.    2.Deeplearning requires substantial computing power.

 

A big controversies  goes on between machine leaning and deep learning. How do they differ? Let’s have a look on their differences.

 

DEEP LEARNING Vs MACHINE LEARNING:

1. DEEP LEARNING:

  • ·         Subset of Machine Learning.
  • ·         Uses neural network that passes data through processing layers to the interpret data features and relations.
  • ·         Algorithms are self depicted on data analysis once they are put into production.
  • ·         Output: Numerical values like text, sound.

                       

2. MACHINE LEARNING:

  • ·         Superset of Machine Learning.
  • ·         Uses various types of automated algorithms that turn to model functions and predict future action from data.
  • ·         Algorithms are detected by data analysts to examine specific variables in data sets.
  • ·         Output: Numerical values like classification of score.

 

HOW DOES DEEP LEARNING WORKS?

 Let’s consider an example of human brain, it is made of neurons. A single neuron in the human brain receives thousands of signals from other neurons.  Like wise, deep leaning consists of neural networks of several nodes. The number of nodes decides how deep the layer is .In an artificial neural network, signals travel between nodes and assign corresponding weights. The heavier weighted node will exert more effect on next layer of nodes. The final layer compiles the weighted inputs and produce the required output.

 Deep learning requires powerful hardware and systems require large amounts of data to return accurate results; accordingly information is fed as huge data sets. When processing the data, artificial neural networks are able to classify data with the answers received from a series of binary true or false questions involving highly complex mathematical calculations. For example,

 a facial recognition program works by learning to detect and recognize lines of faces, then more significant parts of the faces, and, finally, the overall representations of faces. Over time, the program trains itself, and the probability of correct answers increases. In this case, the facial recognition program will accurately identify faces with time.

 

APPLICATIONS OF DEEP LEARNING:

 

·         Self driving cars

·         Natural Language Processing

·         Fraud detection

·         Healthcare

·         Detecting developmental delay in children

·         Demographic  and election predictions

·         Pixel restoration

·         Adding sound to silent movies

·         Visual recognition

·         Virtual Assistants

 

Real-world deep learning applications are a part of our daily lives, but in most cases, they are so well-integrated into products and services that users are unaware of the complex data processing that is taking place in the background. Deep learning is only in its infancy and, in the decades to come, will transform society. Deep learning applications will even save lives as they develop the ability to design evidence-based treatment plans for medical patients and help detect cancers early. So start exploring, deep learning has been able to conquer some very tough challenges, and that's a great reason for being optimistic.

Keep exploring… Thanks for reading !!..Hope you liked this blog, share your thoughts and stay engaged for more blogs.

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