DL Simplified

Deep Learning, Simplified: How to Explain 20+ Models in an Interview

Photo by Crawford Jolly on Unsplash

Deep learning powers some of the most groundbreaking AI applications today — from voice assistants to self-driving cars. At the heart of this revolution are powerful neural network architectures designed to replicate human brain functionality. This article breaks down the most influential deep learning models, providing you with clear and concise explanations to help you confidently tackle technical interview questions.

Perceptron

A Perceptron is the most basic building block of a neural network, often referred to as an artificial neuron. It consists of multiple input nodes, a weighted sum function, and an activation function that determines the output. It is designed to solve binary classification problems (like distinguishing between cats and dogs) by learning a decision boundary. The model updates its weights using the Perceptron Learning Algorithm until it can classify data points correctly.

Figure 1: Perceptron Network

Multilayer Perceptron

A Multilayer Perceptron (MLP) consists of three or more layers — an input layer that receives data…