{"id":508,"date":"2020-05-11T07:25:00","date_gmt":"2020-05-11T04:25:00","guid":{"rendered":"https:\/\/www.dataplatform.gr\/?p=508"},"modified":"2026-02-24T11:20:47","modified_gmt":"2026-02-24T08:20:47","slug":"ti-einai-ena-neyroniko-diktyo-sti-techniti-noimosyni","status":"publish","type":"post","link":"https:\/\/www.dataplatform.gr\/en\/ti-einai-ena-neyroniko-diktyo-sti-techniti-noimosyni\/","title":{"rendered":"What is a neural network in artificial intelligence"},"content":{"rendered":"<p>We often hear about artificial intelligence (<strong>AI<\/strong>), in games, mobiles and various applications but what exactly is it and what does it serve us?<\/p>\n\n\n\n<p>In programming language <strong>Python <\/strong>we have several AI libraries. These make use of neural networks with libraries such as <strong>Tensorflow, Keras, pyTorch, scikit<\/strong>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Models<\/strong><\/h2>\n\n\n\n<p>The first thing we should do using them is to make one <strong>model<\/strong>. This model when structurally ready should be trained (<strong>trained<\/strong>) with one <strong>dataset<\/strong> (amount of data). Depending on how many times it (the model) is trained, its accuracy will increase. The number of trainings you name <strong>epochs<\/strong>.<\/p>\n\n\n\n<p>We can make our own model, or use one of the ready-made ones in the libraries. We can do it too <strong>train <\/strong>or use it <strong>pretrained<\/strong>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Machine learning vs Deep learning<\/h2>\n\n\n\n<p>In an AI model we have one <strong>input layer<\/strong> which we feed the data. Then at least one layer that does the conversion (<strong>hidden layer<\/strong>) and finally an output. In this case we are talking about <strong>machine learning<\/strong>.<\/p>\n\n\n\n<p>In case we have more than one&nbsp;<strong> hidden layer<\/strong>&nbsp; then we are talking about <strong>deep learning<\/strong>. <\/p>\n\n\n\n<p>That<strong> deep learning<\/strong> you use in cases where we need to filter multiple times so we can have a conclusion.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">CNNs for a sixth sense <\/h2>\n\n\n\n<p>There are many types of neural networks but we will specifically deal with them <strong>CNN<\/strong> (<strong>convolutional neural networks<\/strong>).<\/p>\n\n\n\n<p> We see their use every day. In recognition of text, objects\/persons in photos and videos, copyright recognition in music and much more.<\/p>\n\n\n\n<p>The network in each <strong>hidden layer<\/strong> divides the image into small panels called my <strong>tensors<\/strong>. With a filter (usually a 3\u00d73 matrix) called&nbsp; <strong>kernel<\/strong> (something like Instagram ones). The filter shapes the image by passing over each tensor.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1254\" height=\"545\" src=\"https:\/\/www.dataplatform.gr\/wp-content\/uploads\/2020\/05\/cnnfilter.png\" alt=\"\" class=\"wp-image-510\" srcset=\"https:\/\/www.dataplatform.gr\/wp-content\/uploads\/2020\/05\/cnnfilter.png 1254w, https:\/\/www.dataplatform.gr\/wp-content\/uploads\/2020\/05\/cnnfilter-300x130.png 300w, https:\/\/www.dataplatform.gr\/wp-content\/uploads\/2020\/05\/cnnfilter-1024x445.png 1024w, https:\/\/www.dataplatform.gr\/wp-content\/uploads\/2020\/05\/cnnfilter-768x334.png 768w\" sizes=\"auto, (max-width: 1254px) 100vw, 1254px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Activation functions <\/h2>\n\n\n\n<p>As a result, one has been created <strong>activation map<\/strong>&nbsp; with the modulated tensors. Then one is applied <strong>activation function<\/strong> which is usually the <strong>ReLu<\/strong>. Its use <strong>ReLu <\/strong>it simply puts zero to any value in the array (<strong>tensor<\/strong>) is below zero.&nbsp; <\/p>\n\n\n\n<p>Then another function called is applied <strong>max pooling<\/strong>. Using it essentially condenses the table into fewer dimensions, keeping the maximum value according to the step we have set.<\/p>\n\n\n\n<p>It is selected <strong>maxpooling<\/strong> 1D, 2D, 3D depending on whether we convert to text, image or video respectively.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1025\" height=\"549\" src=\"https:\/\/www.dataplatform.gr\/wp-content\/uploads\/2020\/05\/relu.png\" alt=\"\" class=\"wp-image-511\" srcset=\"https:\/\/www.dataplatform.gr\/wp-content\/uploads\/2020\/05\/relu.png 1025w, https:\/\/www.dataplatform.gr\/wp-content\/uploads\/2020\/05\/relu-300x161.png 300w, https:\/\/www.dataplatform.gr\/wp-content\/uploads\/2020\/05\/relu-768x411.png 768w\" sizes=\"auto, (max-width: 1025px) 100vw, 1025px\" \/><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"833\" height=\"373\" src=\"https:\/\/www.dataplatform.gr\/wp-content\/uploads\/2020\/05\/maxpooling.png\" alt=\"\" class=\"wp-image-512\" srcset=\"https:\/\/www.dataplatform.gr\/wp-content\/uploads\/2020\/05\/maxpooling.png 833w, https:\/\/www.dataplatform.gr\/wp-content\/uploads\/2020\/05\/maxpooling-300x134.png 300w, https:\/\/www.dataplatform.gr\/wp-content\/uploads\/2020\/05\/maxpooling-768x344.png 768w\" sizes=\"auto, (max-width: 833px) 100vw, 833px\" \/><\/figure>\n\n\n\n<p>This whole process belongs to the first <strong>hidden layer<\/strong>. We may have more and the corresponding procedure is done in a second, third, etc. <\/p>\n\n\n\n<h2 class=\"wp-block-heading\">The result<\/h2>\n\n\n\n<p>Just before the result we have the <strong>flatten layer<\/strong>. There, all the data we have collected is compared with all the possible answers. In the end depending on what has the highest probability it gives us this as an answer to the output. This process is done through the function <strong>soft max<\/strong> the <strong>sigmoid<\/strong>.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1289\" height=\"572\" src=\"https:\/\/www.dataplatform.gr\/wp-content\/uploads\/2020\/05\/neuron_example.png\" alt=\"\" class=\"wp-image-513\" srcset=\"https:\/\/www.dataplatform.gr\/wp-content\/uploads\/2020\/05\/neuron_example.png 1289w, https:\/\/www.dataplatform.gr\/wp-content\/uploads\/2020\/05\/neuron_example-300x133.png 300w, https:\/\/www.dataplatform.gr\/wp-content\/uploads\/2020\/05\/neuron_example-1024x454.png 1024w, https:\/\/www.dataplatform.gr\/wp-content\/uploads\/2020\/05\/neuron_example-768x341.png 768w\" sizes=\"auto, (max-width: 1289px) 100vw, 1289px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">So what good is a neural network?<\/h2>\n\n\n\n<p>With the neural network, as we saw in the example, we were able, through this whole process, to get an answer to the question of whether the tiger in the photo has brown eyes. <\/p>\n\n\n\n<p>Depending on the accuracy of the model, this answer has a higher probability of being correct. <\/p>\n\n\n\n<p>Using the image and sound data we have collected. The application can solve complex problems such as categorization, text and speech reading.<\/p>","protected":false},"excerpt":{"rendered":"<p>Many times we hear about artificial intelligence (AI), in games, mobiles and various applications, but what exactly is it and what does it serve us? In Python programming language we have several artificial intelligence libraries. These make use of neural networks with libraries such as Tensorflow, Keras, pyTorch, scikit. Models The first thing we should do with their use is [...]<\/p>","protected":false},"author":1,"featured_media":692,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[13],"tags":[25,28,26],"class_list":["post-508","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-datascience_ai","tag-data-science","tag-deep-learning","tag-neural-networks"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>\u03a4\u03b9 \u03b5\u03af\u03bd\u03b1\u03b9 \u03ad\u03bd\u03b1 \u03bd\u03b5\u03c5\u03c1\u03c9\u03bd\u03b9\u03ba\u03cc \u03b4\u03af\u03ba\u03c4\u03c5\u03bf \u03c3\u03c4\u03b7 \u03c4\u03b5\u03c7\u03bd\u03b7\u03c4\u03ae \u03bd\u03bf\u03b7\u03bc\u03bf\u03c3\u03cd\u03bd\u03b7 - DataPlatform.gr<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.dataplatform.gr\/en\/ti-einai-ena-neyroniko-diktyo-sti-techniti-noimosyni\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u03a4\u03b9 \u03b5\u03af\u03bd\u03b1\u03b9 \u03ad\u03bd\u03b1 \u03bd\u03b5\u03c5\u03c1\u03c9\u03bd\u03b9\u03ba\u03cc \u03b4\u03af\u03ba\u03c4\u03c5\u03bf \u03c3\u03c4\u03b7 \u03c4\u03b5\u03c7\u03bd\u03b7\u03c4\u03ae \u03bd\u03bf\u03b7\u03bc\u03bf\u03c3\u03cd\u03bd\u03b7 - DataPlatform.gr\" \/>\n<meta property=\"og:description\" content=\"\u03a0\u03bf\u03bb\u03bb\u03ad\u03c2 \u03c6\u03bf\u03c1\u03ad\u03c2&nbsp; 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