[New] Concise and Practical AI/ML

Share

Explore

Practical Cases

Classic

Classification

Regression

Pattern Learning

Image Classification

Object Detection

Optical Character Recognition (OCR)

Segmentation

Transformation

Image-to-Image

Sequence Learning

Natural Language Processing (NLP)

Voice Recognition

Sequence to Sequence

Text Translation

Algorithm Approximator

Each algorithm can be understood as a function. A network is also a function thus a network can learn to work in place of an algorithm, however, neuralnet will return roughly results instead of exact results thus it can be called algorithm approximator.

The Benefit

Using neuralnet as algorithm approximator has a big benefit as:

Classic algorithm takes intensive amount of time to give output

Neuralnet takes a tiny amount to time for a feed to give output

The Drawback

Neuralnet as algorithm approximator has drawback too

Classic algorithm may/or may not give exact results

Neuralnet always gives approximate results, never exact.

Generative

GAN

Pattern Learning

Sequence Learning

Algorithm Approximator

Share

Want to print your doc?
This is not the way.

Try clicking the ⋯ next to your doc name or using a keyboard shortcut (

CtrlP

) instead.