Answered: Machine learning vs deep learning

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

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Machine learning vs deep learning?

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Machine Learning:

Machine learning is a subset of Artificial Intelligence (AI) applications that provide the system with the ability to learn and improve from experience without being programmed to that level.

Data is used by Machine Learning to train and find accurate results.
Machine learning focuses on the creation of a computer program that accesses and uses information to learn from itself.

Deep Learning:

Deep Learning is a subset of Machine Learning in which the recurrent neural network, the artificial neural network, is connected.

Algorithms are created exactly like machine learning, but algorithms consist of many more levels.
All of these algorithm networks are together referred to as the artificial neural network.
In much simpler terms, as all the neural networks in the brain are linked, it replicates just like the human brain, precisely the concept of deep learning.
With the help of algorithms and its process, it solves all the complicated issues.

Machine Learning vs Deep Learning;

Machine Learning	Deep Learning
Deep learning is a superset of machine learning.	A subset of machine learning is Deep Learning.
Compared to Deep Learning, the data represented in Machine Learning is quite different because it uses structured data.	In Deep Learning, data representation is quite different because it uses neural networks (ANN).
Machine learning is an AI evolution.	In Machine Learning, deep learning is an evolution. It's essentially how deep the learning of the machine is.
Thousands of data points consist of machine learning.	Millions of data points in Big Data.
Numerical Value Outputs, like score classification	Anything, such as free text and sound, from numerical values to free-form elements.
Uses different types of automated algorithms that turn to model features and predict data for future action.	Use the neural network to interpret data features and relationships by passing data through processing layers.
To examine specific variables in data sets, algorithms are detected by data analysts.	Once they are put into production, algorithms are largely self-depicted in data analysis.
To stay in the competition and learn new things, Machine Learning is highly used.	Deep Learning solves problems with complex machine learning.

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