Deep Learning Demystified in a Nutshell
Published October 30,2017 1 year ago Posted By Justinas DanisReading Time: 2 minutes
Until now, Deep Learning is the ultimate extension of AI followed by Machine learning (ML) which doesn’t require many human programmers to explicitly build data construction; rather it learns from experience gathered from task performing.
Deep Learning consists of multiple layers of deep neural networks which are based on logical binary true/ false quotients that computer understands and also very similar to the neural system of human brain.
What is the difference between Machine Learning & Deep Learning?
Machine learning implementation is restricted in the areas where feature explanation is required in regards to fetch better accuracy results. Contrary to this, deep learning excels in breaking the methods of feature extraction from experiences through its deep multi-layered logical binary construction of data.
How Deep Learning works?
DL feeds bulk data to computer that helps system to derive features from to provide answer, accurate to the questions asked.
Deep learning simulates the act of human brain like it does to identify voice, face, hand writing etc. It has the potential to automatically find out relevant features via deep neural networks without the need of manual intervention, opposed to ML.
Data scientists are trying to take Deep Learning to the next level. Tasks including low-level control to higher perception based activities; deep learning program system can learn by themselve using data provided by neural networks.
“There will be time soon, when people with no driving experience or disabled one can take a drive on a car maneuvered by deep learning technology.”
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