Deep Learning _ Perceptrons – day 9

Hello Introduction to Deep Learning and Neural Networks with a Focus on Perceptrons Deep Learning is a subset of machine learning that uses neural networks with many layers (hence “deep”) to model and understand complex patterns in data. These networks are inspired by the human brain and are particularly powerful for tasks like image and speech recognition. Neural Networks consist of interconnected layers of nodes, or neurons. Each neuron receives input, processes it, and passes it to the next layer. The simplest form of a neural network is the Perceptron, which is a single-layer neural network used for binary classification tasks. Perceptron Explained A Perceptron is a fundamental unit of a neural network, performing binary classification by making predictions based on a linear predictor function. It works by: Receiving Input: Taking input features . Weight Multiplication: Multiplying each input by a corresponding weight . Summation: Summing the weighted inputs and adding a bias term . Activation Function: Passing the result through an activation function (typically a step function for a perceptron). The mathematical formula for a perceptron can be written as: where is the activation function. Training a Perceptron Training involves adjusting the weights and bias to minimize classification errors…