Black Box Models Vs White Box Models

Imagine you’re trying to understand how a self-driving car works. You know it can take you safely from point A to point B, but have you ever wondered how it makes decisions on the road? Is it like a magic box that just works, or is there a way to peek inside and see how it thinks? In the world of deep learning, there are two types of models that can help us understand how artificial intelligence works: Black Box Models and White Box Models. In this blog, we’ll explore what these models are, how they work, and their key differences in simple technical words. Whether you’re a curious beginner or a seasoned expert, this blog will help you understand the basics of deep learning and make informed decisions about which model to use. Let’s get started!!

You can read the complete blog using “Friend Link” in case you are not a member of medium yet!!

Black box model vs white box model

1. Black box Model

Imagine you have a magic box that can answer any question you ask it, but you have no idea how it works. You put in some information, and out comes an answer. That’s basically what a black box model is.

In deep learning, a black box model is a type of artificial intelligence that uses complex algorithms to make predictions or decisions without revealing how it arrives at those conclusions. It’s like a super-smart computer that can learn

Create an account to read the full story.

The author made this story available to Medium members only.
If you’re new to Medium, create a new account to read this story on us.

Or, continue in mobile web

Already have an account? Sign in

Published in Artificial Intelligence in Plain English

New AI, ML and Data Science articles every day. Follow to join our 3.5M+ monthly readers.

Written by Jyoti Dabass, Ph.D.

Researcher and engineer with an interest in data science, analytics, marketing, image analysis, computer vision, fuzzy logic, and natural language processing.

No responses yet

What are your thoughts?