Foundation of ml neurons

A developer explains the foundational concept of a neuron in machine learning using a pizza store analogy, where the weight represents a rule (e.g., two pizzas per customer) and training adjusts the weight based on error and input. The post details the core update formula—weight = weight + (error * input * learning_rate)—and clarifies why the adjustment is proportional to the input, tying it to gradient descent.

As we know a neuron in machine learning take a input and predict a output. We are first discuss about the input and process of neurons then we will discuss about training the neuron. Listen to me when you understand this analogy you understand the neuron. Just hold tight with me. Okay? You will get it. Don't give up. It written for those who are struggling with fundament but know something or basic in neural network. Suppose some number of customers come in pizza store. Simple?, you created a rule if one person come to buy pizza I will give 2 pizza to them it's constant and rigid you can't buy one pizza for one person because it's rule of pizza store. You aim for make 30 pizza a day but 20 pizza only get sold in a day. How you sold the pizza of a day what was the math? Number of custombers come multiple 2 pizza per person. That give you your total pizza sold for a day. Now this is very fundamental concept of machine larning try to understand this if 0 customber come a day then your rule of 2 pizza per person is not to blame. Because customber don't come. You don't even blame anything. Your left pizza or error in your aim and pizza that is sold is 30 which is aimed for pizza minus 20 which is sold. Now when come to blaming the rule of pizza. If 0 person come then 2 pizza per person sold total 2 0 = 0 pizza sold. So blame for your rule is 0. But what if 10 person come in pizza store? your rule will be same. Now again you aim for 30 pizza to be sold be only 20 is sold. How you calculate the 20? it's 10 person come multiply by 2. Now it's clearly your rule is for blame neuron training is about how to twist that rule so I get full aimmed pizza sold. So, let's talk about how to twist it. In machine learning you first consider your weight at this: weight = 2 Because 2 pizza per person. the rule error = aimed pizza - total sold adjustment = error Input Then you update the weight or rule weight = weight + adjustment learning rate The above is core of training let me clarify how it's realated to analogy and how whole picture look. adjustment = error input it's your derevitive and when you update the weight it will gradient descent there. Why error input? adjustment is part of new rule which is going to be added on weight itself. So remember when I told you when 0 customber come and then rule is constant then wight is not to be blamed. so if input is 0 then new adjustment in weight look like not changed. but if 10 person come it will change. Okay, bye thank you. Hope it will help you and save you some time, and cognitive energy.