Understanding Perceptrons: The Building Blocks of Neural Networks 🤖

Code - https://github.com/campusx-official/100-days-of-deep-learning/tree/main/day3

Digital Notes for Deep Learning: https://shorturl.at/NGtXg

A Perceptron is a simple type of artificial neural network algorithm developed by Frank Rosenblatt in 1957. It's the basic unit of a neural network, taking multiple binary inputs and producing a single binary output. It computes a weighted sum of its input, applies an activation function, and produces an output.

Perceptron vs. Neuron:

Perceptron: Refers specifically to the algorithm developed by Rosenblatt, typically using a step function as the activation.
Neuron: A more general term used in the context of biological and artificial neural networks. It encompasses various activation functions beyond the step function.

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#Perceptron #Neuron #NeuralNetworks #MachineLearning #ArtificialIntelligence #DataScience

⌚Time Stamps⌚

0:00 Introduction
2:08 What is a Perceptron?
14:30 Neuron Vs Perceptron
22:52 Geometric Intuition
33:53 Code Example
38:04 Outro