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What is Deep Learning? Let's know about Deep Neural Network.

What is Deep Learning? Let's know about Deep Neural Network.

Deep Neural Network.
Deep Neural Network

What is Deep Learning?

Deep learning is a sub-field of Machine Learning and an aspect of Artificial Intelligence.  To understand this more easily, understand that it is meant to emulate the learning approach that humans use to acquire certain types of knowledge.

This is somewhat different from Machine Learning, often people get confused in this and Machine Learning. Deep learning uses a sequencing algorithm while Machine Learning uses a linear algorithm.

To understand this more accurately, understand this example that if a child is identified with a flower, then he will ask again and again, is this flower? For him, every colorful thing will be a flower, he will slowly index things according to the flowers and slowly he will know the flower. It develops over time.

How does deep learning work? 

Deep learning work
Deep learning work

Deep learning : Each algorithm applies a non-linear transformation to its input and converts it into a statistical model from what it learns from the input. And it continues its effort until the exact output is found.

Whereas in traditional Machine Learning, the learning process is monitored and the programmer should be very specific when telling the programmer what kinds of things to search when making decisions.

This is a laborious process called feature extraction and the success rate of a computer depends entirely on the ability of the programmer to define a feature.

The advantage of Deep learning is that the program creates a self-determined facility without supervision. Not only is untrained education faster, but it is also generally more accurate.

For example, suppose you make the computer familiar with the shape of a flower, but it makes a pattern not from its petals or designs, but from the pixels, which it helps to understand.

Uses of Deep Learning

Uses of Deep Learning
Uses of Deep Learning

Deep learning is being used quite rapidly in today's time, almost every big company is using it, or wants to do it.

Some of its recent big usage has been done by the big phone companies, which include these things.

Image Recognition : This means recognizing a picture, it can often be seen easily in mobile phones.

Speech Recognition : Its job is to recognize the voice of the people.

Translator : Its function is to convert one language into another language. Many more examples of Deep learning can also be seen.

Limits of Deep Learning 

The biggest limitation of Deep learning is that it learns only through observation. This means that the data given to it knows only that much.

If no one has a large amount of data available then it will not work in that condition. If the data is collected in a biased way, then the result obtained will also be more inclined towards any one. That is, whatever you give it, it will learn from it and will give you results.

What is Deep Neural Networking?

Deep Neural Networking
Deep Neural Networking

The way of thinking of Deep learning is exactly like human neuron, so it is often called Deep Neural Learning and Deep Neural Networking.

It may take a few days for a small child to consider a flower as a flower, but Deep Neural Networking can identify a flower picture in a few minutes out of millions of pictures.

To do this one has to achieve an acceptable level of accuracy for which Deep learning programs require access to an enormous amount of training data and processing power.  Earlier it was not so easy but in the century of cloud computing and large data base it is easily done.

Unstructured data can also be used quite easily through Deep Neural Networking.  By the way, most of the data collected is unstructured.

Earth man is said to be the fastest and most sensible organism. In this computer science era, today we want to make the computer or any machine as much better as possible, even today, we have become capable that today our computer also gives commands automatically and its corresponding work  Also does it himself.

Neural Network is also a type of information processing. It functions just like a human's brain, just as a human's brain processes information, this network also works.

Deep Neural Network definition

Neural Networks are interconnected / interrelated neurons. And the Artificial Neural Network is a computerized tool built on top of the Neural Network itself. Simply put, it is composed of a large number of interconnected neurons working in unison to solve specific problems.

We also call Artificial Neural Network as Neural Nets. Parallel distributed processing system, connectionist system is also a parallel term.

Beginning of Deep Neural Network 

Deep Neural Network
Deep Neural Network 

The first Neural Network was produced in 1943 by neuro physiologists "Warren McCulloch" and logician "Walter Pitts".  But at that time, technology did not allow them to do much.

Advantages of Neural Network 

Neural Networks can be used to derive meaning from complex or unfamiliar data, as well as to extract patterns and detect things that cannot be seen by humans or other simple computer technologies.

Initially we can use Neural Networks to work or learn based on some data.

Neural Networks can create and represent its organization from the information received during learning time.

Neural Networks is a tool for non-linear statistical data modeling. Complex data analysis is performed by this model.

How to Learn Neural Network 

Neural Networks learn by example. They cannot be programmed to perform a specific task.

Examples must be carefully selected, otherwise useful time will be wasted or the network will function incorrectly.

Uses of Neural Network 

Uses of Neural Network
Uses of Neural Network 

Neural Network is used in modeling and designing a solar steam generating plant used in the field of solar energy.

Neural Networks are used in pattern recognition systems, data processing, robotics, modeling, etc.

Neural Networks are flexible and situation adaptive or in simple way may called adaptive.

Artificial Neural Network acquires knowledge from around itself by adopting internal and external standards and also solving complex problems that are difficult to restrict.

Flexibility : Neural Networks are flexible and have the ability to synchronize and adapt learning situations based on conclusions.

The Neural Network fully depend on adaptive learning.

Neural Networks already has the ability and knowledge to produce an adequate response in an unknown situation.

Engineering point of Neural Network 

Engineering point of Neural Network
Engineering point of Neural Network 

If we look at Neural Networks from an engineering point of view, Neural Network is a device that has many inputs and outputs. There are two modes of operation of a neuron: training mode or usage mode.

In training mode the neuron can be further trained for particular input patterns. In usage mode, when a taught input is detected on the input, the output corresponding to it becomes the current output.

Difference between Traditional and Neural Network 

Traditional and Neural Network
Traditional and Neural Network 

Neural Networks are more tolerant than traditional networks. The network is capable of regenerating or regenerating fault in any of its components without any loss of all data.

The main motive and intention behind its development is only that the Neural Network is computed along with the biological neuron.

Neural Networks are no miracles but if used wisely they can produce some amazing results.

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