{"id":45118,"date":"2024-04-15T21:53:29","date_gmt":"2024-04-15T21:53:29","guid":{"rendered":"https:\/\/exam.pscnotes.com\/mcq\/?p=45118"},"modified":"2024-04-15T21:53:29","modified_gmt":"2024-04-15T21:53:29","slug":"having-multiple-perceptrons-can-actually-solve-the-xor-problem-satisfactorily-this-is-because-each-perceptron-can-partition-off-a-linear-part-of-the-space-itself-and-they-can-then-combine-their-resu-2","status":"publish","type":"post","link":"https:\/\/exam.pscnotes.com\/mcq\/having-multiple-perceptrons-can-actually-solve-the-xor-problem-satisfactorily-this-is-because-each-perceptron-can-partition-off-a-linear-part-of-the-space-itself-and-they-can-then-combine-their-resu-2\/","title":{"rendered":"Having multiple perceptrons can actually solve the XOR problem satisfactorily: this is because each perceptron can partition off a linear part of the space itself, and they can then combine their results."},"content":{"rendered":"<p>\r\n    <!-- Check if it's an AMP page -->\r\n            <!-- Non-AMP version -->\r\n        <div class=\"mcq-container\" data-quiz-id=\"quizState_6a9787dfceb9f\">\r\n                                            <div class=\"option\" data-option-key=\"option1\" data-is-correct=\"false\">\r\n                    true - this works always, and these multiple perceptrons learn to classify even complex problems                <\/div>\r\n                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            <div class=\"option\" data-option-key=\"option3\" data-is-correct=\"false\">\r\n                    true - perceptrons can do this but are unable to learn to do it - they have to be explicitly hand-coded                <\/div>\r\n                                            <div class=\"option\" data-option-key=\"option4\" data-is-correct=\"false\">\r\n                    false - just having a single perceptron is enough                <\/div>\r\n                            \r\n            <!-- Feedback messages for non-AMP -->\r\n            <div class=\"feedback\" data-feedback=\"wrong\">Answer is Right!<\/div>\r\n            <div class=\"feedback\" data-feedback=\"right\">Answer is Wrong!<\/div>\r\n        <\/div>\r\n\r\n        <script>\r\n        document.addEventListener('DOMContentLoaded', function () {\r\n            var containers = document.querySelectorAll('.mcq-container');\r\n\r\n            containers.forEach(function(container) {\r\n                var options = container.querySelectorAll('.option');\r\n                var feedbackSelect = container.querySelector('[data-feedback=\"select\"]');\r\n                var feedbackWrong = container.querySelector('[data-feedback=\"wrong\"]');\r\n                var feedbackRight = container.querySelector('[data-feedback=\"right\"]');\r\n\r\n                options.forEach(function(option) {\r\n                    option.addEventListener('click', function() {\r\n                        var selectedOption = option.getAttribute('data-option-key');\r\n                        var isCorrect = option.getAttribute('data-is-correct') === 'true';\r\n\r\n                        \/\/ Remove previous selections\r\n                        options.forEach(function(opt) {\r\n                            opt.classList.remove('correct', 'incorrect');\r\n                        });\r\n\r\n                        \/\/ Add the correct\/incorrect class\r\n                        if (isCorrect) {\r\n                            option.classList.add('correct');\r\n                            feedbackRight.hidden = false;\r\n                            feedbackWrong.hidden = true;\r\n                        } else {\r\n                            option.classList.add('incorrect');\r\n                            feedbackRight.hidden = true;\r\n                            feedbackWrong.hidden = false;\r\n                        }\r\n\r\n                        \/\/ Hide select feedback\r\n                        feedbackSelect.hidden = true;\r\n                    });\r\n                });\r\n            });\r\n        });\r\n        <\/script>\r\n    \r\n    <!--more--><\/p>\n<p>The correct answer is: <strong>B. false &#8211; perceptrons are mathematically incapable of solving linearly inseparable functions, no matter what you do<\/strong><\/p>\n<p>A perceptron is a type of artificial neuron that can be used to classify data. It has a single input, a single output, and a weight associated with each input. The output of the perceptron is calculated by multiplying the inputs by their weights and then adding them together. If the sum is greater than a certain threshold, the output is 1; otherwise, the output is 0.<\/p>\n<p>A linearly inseparable function is a function <div class=\"telegram-channel-container\">\r\n        <a href=\"https:\/\/t.me\/pscnotes2025\" target=\"_blank\" class=\"telegram-channel-button\">\r\n            <span class=\"telegram-icon\">\r\n                <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 496 512\">\r\n                    <path fill=\"white\" d=\"M248,8C111,8,0,119,0,256s111,248,248,248s248-111,248-248S385,8,248,8z M362,177L320,367c-3,14-10,18-20,14l-56-41l-27,26 c-3,3-5,5-10,5l4-63L323,196c5-5-1-7-8-3l-98,62l-42-13c-9-3-10-9,2-14l162-63C351,160,365,164,362,177z\"\/>\r\n                <\/svg>\r\n            <\/span>\r\n            Join Our Telegram Channel\r\n        <\/a>\r\n    <\/div> that cannot be represented by a linear combination of perceptrons. The XOR function is an example of a linearly inseparable function.<\/p>\n<p>No matter how many perceptrons you use, you cannot solve the XOR problem with a single layer of perceptrons. This is because the XOR function is not linearly separable.<\/p>\n<p>There are a few ways to solve the XOR problem with neural networks. One way is to use a two-layer neural network. In a two-layer neural network, the first layer of perceptrons is followed by a second layer of perceptrons. The outputs of the first layer are connected to the inputs of the second layer. The weights of the connections between the layers are learned during training.<\/p>\n<p>Another way to solve the XOR problem with neural networks is to use a convolutional neural network (CNN). A CNN is a type of neural network that is specifically designed for image recognition. CNNs have been shown to be very effective at solving a variety of problems, including the XOR problem.<\/p>\n<p>In conclusion, perceptrons are mathematically incapable of solving linearly inseparable functions, no matter what you do. The XOR function is an example of a linearly inseparable function. There are a few ways to solve the XOR problem with neural networks, such as using a two-layer neural network or a convolutional neural network.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Join Our Telegram Channel Subscribe on YouTube<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[729],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v22.2 (Yoast SEO v23.3) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Having multiple perceptrons can actually solve the XOR problem satisfactorily: this is because each perceptron can partition off a linear part of the space itself, and they can then combine their results.<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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