Deep Learning vs Machine Learning
Deep Learning is a subtype of Machine Learning focus on Images as data.
|Machine Learning||Deep Learning|
|How does it work?||Use types of automated algrithms which learn to predict future decisions and model function using the data fed to it.||Interprets data features and its relationships using neural networks which pass the relevant information through several stages of data processing.|
|Management||The various algorithms are directed by the analysis to examine the different variables in the dataset||Once they are implemented, the algorithms are usually self-directed for the relevant data analysis|
|Number of Data Points||Usually, there are a gew thousand data points used for the analysis.||There a few million data points used for the analysis.|
|Output||The output is usually a numerical value, like a score or a classification||The output can be anything from a score, an element, free text or sound, etc.|
Deep Learning Types
Deep Learning Models/Algorithms
There is a different models or approach those define algorithms based on mathematics, statistics and matrix models in deep learning.
Unsupervised Pretrained Networks
See main article UPN.
Convolutional Neural Networks
Is the model based on ANN to solve complex problems which depends of data extracted from images.
See main article CNN.