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Deep Learning is the paradigm of AI

Today is the golden era of artificial intelligence. This is the latest trend in Sri Lanka. Deep learning is the paradigm of artificial intelligence and it is the hot topic everywhere. Everyone talks about it in every industry in the world. What is the deep learning? Why use it? Where to go? In every industry, time is the critical factor and accuracy is another critical factor. These two factors can decide about earning profit to company or not. Misusage of those factors leads to fall down the company to hell within few seconds. We need right information at right time. Deep learning is the technology that saves time and improves accuracy in different applications. This technology replaces the key characteristics of traditional machine learning. Without this technology, how to train computers to identify speech, images, handwritten letters, text quickly and accurately??? We train the computers that can perform intelligent abilities as human. Those computers are faster than human when dealing with big data sets. The companies namely Google, Facebook have kept the first step of this era. This technology will be interesting and important with big data analytics in every industry in near future.

 

Conventional Machine Learning vs Deep learning

Conventional-Machine-Learning-vs-Deep-learning

Image taken from devblogs.nvidia.com

 

Machine learning technology was emerged in past and it powers many aspects of modern society. It is usually used in web searches on e-commerce websites, cameras and smart-phones. Identifying objects in images, transcribing speech to text, matching news items are different applications of machine learning. Machine learning requires considerable domain expertise to design features from the raw data (such as the pixel values of an image). Feature engineering of machine learning is difficult and time consuming. Deep learning is emerged as a solution for feature engineering with improved characteristics. It performs with self-learning capabilities and faster processing. In deep learning, feature engineering is automated. Automatically detected new features are transformed through deep architectures to improve overall accuracy in different applications.

 

Applications of Deep Learning

Image taken from cs.toronto.edu

Image taken from cs.toronto.edu

Deep learning is dramatically improved in many applications such as speech recognition, computer vision (image/object recognition), audio and video, natural language, automatic game playing, object classification in photographs, handwriting recognition and text classification. Face recognition and object recognition in photographs are frequently used applications in deep learning.

 

Future of Deep Learning

Image taken from martech advisor

Image taken from martech advisor

Think a moment, how to train computers to play the intelligent capabilities of humans in real world scenarios. It will improve your company profit. It will change the future of your company. It is the challenge. Who can win this????? The person who can keep first step of this era, definitely win the future.

Pamudi Jayathilaka
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