{"slug": "foundation-of-ml-neurons", "title": "Foundation of ml neurons", "summary": "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.", "body_md": "As we know a neuron in machine learning take a input and predict a output.\n\nWe are first discuss about the input and process of neurons then we will discuss about training the neuron.\n\nListen to me when you understand this analogy you understand the neuron.\n\nJust hold tight with me. Okay? You will get it. Don't give up.\n\nIt written for those who are struggling with fundament but know something or basic in neural network.\n\nSuppose 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.\n\nHow 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.\n\nNow 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.\n\nYour left pizza or error in your aim and pizza that is sold is 30 which is aimed for pizza minus 20 which is sold.\n\nNow 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.\n\nBut 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.\n\nNow it's clearly your rule is for blame neuron training is about how to twist that rule so I get full aimmed pizza sold.\n\nSo, let's talk about how to twist it.\n\nIn machine learning you first consider your weight at this:\n\n```\nweight = 2 # Because 2 pizza per person. (the rule)\n\nerror = aimed pizza - total sold\nadjustment = error * Input\n\n# Then you update the weight or rule\nweight = weight + (adjustment * learning_rate)\n```\n\nThe above is core of training let me clarify how it's realated to analogy\n\nand how whole picture look.\n\n```\nadjustment = error * input\n```\n\nit's your derevitive and when you update the weight it will gradient descent there.\n\nWhy 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.\n\nso if input is 0 then new adjustment in weight look like not changed.\n\nbut if 10 person come it will change.\n\nOkay, bye thank you.\n\nHope it will help you and save you some time, and cognitive energy.", "url": "https://wpnews.pro/news/foundation-of-ml-neurons", "canonical_source": "https://dev.to/sumit_gupta_3fa873c3d768e/foundation-of-ml-neurons-36pd", "published_at": "2026-08-11 14:21:33+00:00", "updated_at": "2026-08-11 14:47:41.436924+00:00", "lang": "en", "topics": ["machine-learning", "neural-networks"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/foundation-of-ml-neurons", "markdown": "https://wpnews.pro/news/foundation-of-ml-neurons.md", "text": "https://wpnews.pro/news/foundation-of-ml-neurons.txt", "jsonld": "https://wpnews.pro/news/foundation-of-ml-neurons.jsonld"}}