Meaning of deep learning
Deep learning is a sub-field of machine learning and an aspect of artificial intelligence. To understand this more easily, let us understand that it is meant to simulate the approach of learning that humans use to acquire certain types of knowledge.
This is different from machine learning, often people get confused in this and machine learning. Deep learning uses an indexed algorithm whereas machine learning uses a linear algorithm.
To understand this more accurately, consider this example that if a child is identified with a flower, he will ask again and again, is this a flower? For him every colorful thing will be a flower, he will slowly sort things according to the flowers and slowly he will come to know about the flower. It develops over time.
How does Deep Learning work?
Deep learning Each algorithm applies a non-linear transformation to its inputs and transforms it into a statistical model from what it learns from the input. And it continues its efforts till the exact output is found.
Whereas in traditional machine learning, the learning process is supervised and the programmer has to be very specific while telling the programmer what kind of things to be explored while making a decision.
This is a laborious process called feature extraction and the success rate of a computer depends entirely on the programmer's ability to define a feature.
The advantage of deep learning is that the program creates a feature set by itself without supervision. Unsupervised learning is not only faster, but it is usually more accurate.
For example, suppose you introduce a computer to the shape of a flower, but it doesn't pattern the flower by its petals or design, but with pixels, with the help of which it understands the flower.
What is Deep Neural Networking?
The way of thinking of deep learning is exactly like that of human neuron, so it is often also called deep neural learning and deep neural networking.
It may take a few days for a small child to recognize a flower as a flower, but Deep Neural Networking can recognize a flower out of millions of pictures in a matter of minutes.
To be able to do this an acceptable level of accuracy has to be achieved, for which deep learning programs require access to training data and enormous amounts of processing power. Earlier it was not so easy but in the age of cloud computing and big data base it becomes easy.
Unstructured data can also be accessed quite easily through Deep Neural Networking. However, most of the data collected is unstructured.
uses of deep learning
Deep learning is being used very fast in today's time, almost every major company is using it, or wants to.
Some of its recent big uses have been made by big phone companies, which include these things.
Image Recognition- It means to recognize a picture, it can often be easily seen in mobile phones.
Speech Recognition - Its job is to recognize the voice of the people.
Translator – Its job is to convert one language into another language. Many more examples of deep learning can also be found.
Importance Of Deep Learning :
1. Largest unstructured data can be processed by Deep Learning. Which is difficult to do through machine learning.
2. Complex algorithms can also be easily implemented inside Deep Learning, which is not easy to do through Machine Learning.
3. Deep Learning also increases the performance of machine learning. Because the more data the machine learning has, the slower the machine learning model will be. So here we use Deep Learning to handle more data.
4. In Deep Learning, it extracts this feature from the large amount of data that is given on the basis of input. Whereas the labeled data of Machine Learning is given as input and from that it extracts the feature.
deep learning limits
The biggest limitation of deep learning is that it learns only by observation. This means that it knows only that much of the data it is given.
If someone does not have a large amount of data available then it will not work in that condition. If the data is collected in a biased manner, the result will be tilted more towards either side. That is, whatever you give it, it will learn from it and will give you results.
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