Deep learning, a part of machine learning, deals with artificial neural networks. It aims to mimic the learning pattern of the human brain. Deep learning does not require everything to be programmed explicitly. Although, in the present world programmers are crazy about this concept.
It is believed that within 20 years the processing power of the data, which is lacking today, will be achieved at a great height. so, the demands of Deep learning applications and deep learning applications are expanding exponentially.
The human brain consists of a large network of neuron systems which is very complex and complicated to decode. To create such functionality in the computer world, the computer science expert created an artificial structure known as an artificial neural net which is composed of nodes or neurons.
Just like a human brain, there are some nodes in, some nodes out, and some nodes in between which are interconnected in a very complex manner and are hidden in many layers.
The architecture of deep learning technology
The following passage will discuss the components of deep learning technology
” Deep Neural Network
Deep Neural Network has its own complicity such as hidden layers in between input and output layers. processing and modeling non-linear data is done very efficiently in this network.
“Deep Belief Network( DBN)
In the Deep learning Algorithm, Deep Belief Network plays an important role. DBN is a class of Deep Neural networks. It is a complex multi-layer system.
Steps for executing a DBN
“ With the help of the Contrastive Divergence algorithm, one must know layers of features from visible units.
“ Activation of previously trained features should be treated as the visible units. It is recommended to learn the features of features.
“ After attaining the knowledge of the final hidden layer one can run the whole Deep Belief Network.
Recurrent Neural Network Recurrent can be defined as the performance of the same task of every element of a sequence. Recurrent Neural Network is very compatible with both parallel and sequential computation which is very similar to that of the human brain.
Recurrent Neural Network has the capability of remembering important points about the input that was given to them. This input is then made more accurate and precise by the Recurrent Neural Network.
How does Deep Learning differ from Machine Learning?
“ Deep Learning is capable of working on large amount databases whereas Machine Learning maintains its accuracy by working on a small number of database
“ Deep Learning depends on High-end machines whereas Machine Learning depends on a low-end machine
“ Deep Learning is programmed to solve problems end to end whereas Machine Learning makes many divisions of that task and focuses on solving them individually, then combines all the results to give the outcome.
“ The training time of Deep learning is longer as compared to Machine Learning.
“ Testing time of Deep learning is less as compared to Machine Learning.
Advantages of Deep Learning technology
“ The Deep Learning algorithm is very efficient for in-class performance.
“ It can effectively reduce the requirement for feature engineering.
“ It helps to reduce the redundant cost
“ It can easily detect complex problems.
Disadvantages of Deep Learning technology
“ It requires a large quantity of data
“ Comparably it is expensively to train
“ Till now a strong theoretical foundation has not formed.
“ The issue of biases is prominent.
“ Observation is the only way of learning.
Applications of Deep Learning technology
Deep Learning applications have found their application in different human sectors. Some of them are discussed below
“ In healthcare
With the help of Deep Learning technology diagnosis of complex diseases can be done more efficiently.
“ In automatic text generation
Each word or character of the dead text can be successfully recovered from using this technology.
“ In Automatic Machine Translation
It can efficiently translate words, phrases, and sentences from one language to another.
“ In the prediction of the earthquake
With the help of viscoelastic computation, the earthquake can be predicted accurately.
“ In recognition of the image
It can identify almost any images that are stored in its system. Deep Learning technology finds its best application in the field of image and text.
Deep Learning technology in future
The future of any aspirants who aspire to learn Deep Learning technology is full of potential. The most mesmerizing fact of neural learning is that it deals with a large quantity of dilute data. The population of the world is constantly increasing so managing everything manually is impossible so every organization, governmental or independent, is bending towards artificial intelligence.
The enormous need for managing data is constantly increasing as the demands of the Deep Learning application. So if you are planning to pursue a career in Deep Learning technology then it will be a perfect time. There is an endless possibility in the world of Deep Learning technology.
The IoT Academy is the most suitable place for data science enthusiasts to learn about the different aspects of data science. With insights from industry experts, you can surely work for your dream job in the data science domain.
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