Today most of the artificial intelligence problems are
solved using Deep learning techniques. It provides better results than
traditional Machine learning approaches. As sometimes machine learning does not
perform better as the size and complexity of the data increases. Here Deep
learning plays an important role and provides better results.
Applications of Deep Learning
Some of the most popular applications of Deep learning
are:
Computer vision
Language translation
Image captioning
Audio transcription
Molecular biology
Speech recognition
Natural language processing
Self-driving cars
Brain tumour detection
Real-time speech translation
Music composition
Automatic game playing
There are many more applications of deep learning
techniques in the field of Artificial Intelligence.
Deep learning techniques is the next big leap after
machine learning with a more advanced implementation. Currently, it is heading
towards becoming an industry standard bringing a strong promise of being a game
changer when dealing with raw unstructured data and very large amount of data.
Deep learning is currently one of the best solution
providers for a wide range of real-world problems. Developers are building AI
programs that, instead of using previously given rules, learn from examples to
solve complicated tasks and gives the most accurate results.
Deep learning is being used by many data scientists,
where Deep neural networks are delivering results that are ever more accurate
as compared to others techniques.
The idea behind deep neural networks is to develop
deep neural networks by increasing the number of training layers for different
hidden layers present in the network. Machine learns more about the data until
it is as accurate as possible. Many data scientist and developers use deep
learning techniques to implement complex machine learning tasks, and also to train
AI networks to have high levels of perceptual recognition with desired results.
The most popular application of Deep learning is Computer
vision.
Computer Vision
Image
Classification
Image Classification with Localization
Object Detection
Object Segmentation
Image Style Transfer
Image Colorization
Image Reconstruction
Image Super-Resolution
Image Synthesis
Here one of the main tasks is achieved is the image the classification was given input images are classified into different classes
such as cat, dog, male, female etc.
It’s a very difficult task for a machine to learn how
to classify different images. As we humans learn how to do this task very early
in our lives and have these skills of quickly recognizing patterns,
generalizing from prior knowledge, and adapting to different image
environments. Deep Learning also shows the promising result to classify the
different images.
So, Deep Learning has a wide application in the real world
problems.
I hope you enjoyed reading this article and finally, you came
to know about Deep Learning applications.
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