{"id":28962,"date":"2013-05-23T12:44:06","date_gmt":"2013-05-23T07:14:06","guid":{"rendered":"http:\/\/www.kopykitab.com\/blog\/?p=28962"},"modified":"2023-03-27T14:45:31","modified_gmt":"2023-03-27T09:15:31","slug":"neural-network-notes","status":"publish","type":"post","link":"https:\/\/www.kopykitab.com\/blog\/neural-network-notes\/","title":{"rendered":"Neural Network Notes"},"content":{"rendered":"<h1 style=\"text-align: center;\">Neural Network Notes<\/h1>\n<h2><\/h2>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_47_1 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"ez-toc-toggle-icon-1\"><label for=\"item-69da1e9b5beb0\" aria-label=\"Table of Content\"><span style=\"display: flex;align-items: center;width: 35px;height: 30px;justify-content: center;direction:ltr;\"><svg style=\"fill: #000000;color:#000000\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #000000;color:#000000\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/label><input  type=\"checkbox\" id=\"item-69da1e9b5beb0\"><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 eztoc-visibility-hide-by-default' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.kopykitab.com\/blog\/neural-network-notes\/#neural-network-introduction\" title=\"Neural Network Introduction\">Neural Network Introduction<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.kopykitab.com\/blog\/neural-network-notes\/#why-use-neural-networks\" title=\"Why use neural networks?\">Why use neural networks?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.kopykitab.com\/blog\/neural-network-notes\/#applications-of-neural-networks\" title=\"Applications of neural networks\">Applications of neural networks<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"neural-network-introduction\"><\/span><b>Neural Network Introduction<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>An Artificial Neural Network (ANN) is an information-processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons. This is true of ANNs as well.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"why-use-neural-networks\"><\/span><b>Why use neural networks?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><b>\u00a0<\/b>Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained neural network can be thought of as an &#8220;expert&#8221; in the category of information it has been given to analyze. This expert can then be used to provide projections given new situations of interest and answer &#8220;what if&#8221; questions.<\/p>\n<p>Other advantages include:<\/p>\n<p>Adaptive learning: An ability to learn how to do tasks based on the data given for training or initial experience.<\/p>\n<p>Self-organization: An ANN can create its own organization or representation of the information it receives during learning time.<\/p>\n<p>Real-Time Operation: ANN computations may be carried out in parallel, and special hardware devices are being designed and manufactured which take advantage of this capability.<\/p>\n<p>Fault Tolerance via Redundant Information Coding: Partial destruction of a network leads to the corresponding degradation of performance. However, some network capabilities may be retained even with major network damage.<\/p>\n<p><b>The architecture of neural networks<\/b><\/p>\n<p><b>Feed-forward networks<\/b><\/p>\n<p>Feed-forward ANNs (figure 1) allow signals to travel one way only; from input to output. There is no feedback (loops) i.e. the output of any layer does not affect that same layer. Feed-forward ANNs tend to be straightforward networks that associate inputs with outputs. They are extensively used in pattern recognition. This type of organization is also referred to as bottom-up or top-down.<\/p>\n<p>Feedback network<\/p>\n<p>Feedback networks (figure 1) can have signals traveling in both directions by introducing loops in the network. Feedback networks are very powerful and can get extremely complicated. Feedback networks are dynamic; their &#8216;state&#8217; is changing continuously until they reach an equilibrium point. They remain at the equilibrium point until the input changes and a new equilibrium needs to be found. Feedback architectures are also referred to as interactive or recurrent, although the latter term is often used to denote feedback connections in single-layer organizations.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"applications-of-neural-networks\"><\/span><b>Applications of neural networks<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Neural Networks in Practice<\/p>\n<p>Given this description of neural networks and how they work, what real-world applications are they suited for? Neural networks have broad applicability to real-world business problems. In fact, they have already been successfully applied in many industries.<\/p>\n<p>Since neural networks are best at identifying patterns or trends in data, they are well suited for prediction or forecasting needs including:<\/p>\n<p>sales forecasting<\/p>\n<p>industrial process control<\/p>\n<p>customer research<\/p>\n<p>data validation<\/p>\n<p>risk management<\/p>\n<p>target marketing<\/p>\n<p>But to give you some more specific examples; ANN is also used in the following specific paradigms: recognition of speakers in communications; diagnosis of hepatitis; recovery of telecommunications from faulty software; interpretation of multi-meaning Chinese words; undersea mine detection; texture analysis; three-dimensional object recognition; hand-written word recognition; and facial recognition.<\/p>\n<p>Neural networks in medicine<\/p>\n<p>Neural Networks in business<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Neural Network Notes Neural Network Introduction An Artificial Neural Network (ANN) is an information-processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected &#8230; <a title=\"Neural Network Notes\" class=\"read-more\" href=\"https:\/\/www.kopykitab.com\/blog\/neural-network-notes\/\" aria-label=\"More on Neural Network Notes\">Read more<\/a><\/p>\n","protected":false},"author":4,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"fifu_image_url":"","fifu_image_alt":""},"categories":[4773],"tags":[2839],"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/www.kopykitab.com\/blog\/wp-json\/wp\/v2\/posts\/28962"}],"collection":[{"href":"https:\/\/www.kopykitab.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.kopykitab.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.kopykitab.com\/blog\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/www.kopykitab.com\/blog\/wp-json\/wp\/v2\/comments?post=28962"}],"version-history":[{"count":1,"href":"https:\/\/www.kopykitab.com\/blog\/wp-json\/wp\/v2\/posts\/28962\/revisions"}],"predecessor-version":[{"id":338538,"href":"https:\/\/www.kopykitab.com\/blog\/wp-json\/wp\/v2\/posts\/28962\/revisions\/338538"}],"wp:attachment":[{"href":"https:\/\/www.kopykitab.com\/blog\/wp-json\/wp\/v2\/media?parent=28962"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.kopykitab.com\/blog\/wp-json\/wp\/v2\/categories?post=28962"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.kopykitab.com\/blog\/wp-json\/wp\/v2\/tags?post=28962"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}