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!!
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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…