{"id":71750,"date":"2023-01-26T17:02:23","date_gmt":"2023-01-26T16:02:23","guid":{"rendered":"https:\/\/www.clickworker.com\/?page_id=71750"},"modified":"2023-05-15T11:29:22","modified_gmt":"2023-05-15T10:29:22","slug":"convolutional-neural-network","status":"publish","type":"page","link":"https:\/\/www.clickworker.com\/ai-glossary\/convolutional-neural-network\/","title":{"rendered":"Convolutional Neural Network"},"content":{"rendered":"<style>\r\n.faq-snippet.collapsable div h2, .faq-snippet {color: #1d1d1f;} .faq-snippet p { color: #212529;}\r\n<\/style>\r\n\r\n<section class=\"white section-space top_grey_separation_sm\">\r\n<div class=\"container pb-5\">\r\n<div class=\"row align-items-center justify-content-center justify-content-lg-between\">\r\n<div class=\"col-lg-6 pb-2\">\r\n\r\n<span class=\"gl_letter_offcanvas gl_single\">C<\/span>\r\n<h1 class=\"section-headline pt-4 mt-1 mb-4\"><span class=\"gl_single_headline\">Convolutional Neural Network <\/span><\/h1>\r\n<ul>\r\n\t<li><a href=\"#what\">All About Convolutional Neural Networks<\/a><\/li>\t\t\t\t\t\r\n\t\t\t\t\t<li><a href=\"#what\">What are Convolutional Neural Networks?<\/a><\/li>\t\t\t\t\t\r\n\t\t\t\t\t<li><a href=\"#neuralnetwork\">What is a neural network?<\/a><\/li>\t\r\n\t\t\t\t\t<li><a href=\"#covnets\">Introduction to ConvNets \/ CNNs<\/a><\/li>\r\n\t\t\t\t\t<li><a href=\"#how\">How does CNN work?<\/a><\/li>\r\n\t\t\t\t\t<li><a href=\"#history\">History and breakthroughs<\/a><\/li>\r\n\t\t\t\t\t<li><a href=\"#applications\">Applications and benefits <\/a><\/li>\r\n\t\t\t\t\t<li><a href=\"#challenges\">Challenges and concerns<\/a><\/li>\r\n\t\t\t        <li><a href=\"#conclusion \">Conclusion <\/a><\/li>\r\n\t\t\t\t\t<li><a href=\"#faq\">FAQs<\/a><\/li>\r\n\t\t\t\t\t<\/ul>\t\r\n\t\t\t\t\t\r\n\t\t\t\r\n\t\t\t\t\t\r\n<\/div>\r\n<div class=\"col-auto\">\r\n\t<img decoding=\"async\" src=\"https:\/\/www.clickworker.com\/wp-content\/uploads\/2023\/01\/Convolutional-Neural-Network.png\" alt=\"Convolutional Neural Networks\" class=\"gl_image max-width-100\"><\/div>\r\n<\/div>\r\n<\/div>\r\n<\/section>\r\n\r\n<section class=\"gray section-space\">\r\n<div class=\"container\">\r\n<div class=\"row\">\r\n<div class=\"col-lg-12\">\r\n<h2 class=\"section-headline mb-4\" id=\"what\">What are Convolutional Neural Networks?<\/h2>\r\n<p>The <strong>convolutional neural network<\/strong>, called CNN, is a deep learning algorithm for image recognition tasks. It is a machine learning algorithm using neural networks with multiple-node architecture. CNN is considered to be the best neural network algorithm for image recognition and dealing with pixel data processing. And thus, they have been used in various computer vision (CV) applications such as facial recognition, self-driving cars, and more.<\/p>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/section>\r\n\r\n<section class=\"white section-space bottom_grey_separation_sm\">\r\n<div class=\"container\">\r\n<div class=\"row\">\r\n<div class=\"col-lg-12\">\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"neuralnetwork\">What is a neural network?<\/h2>\r\n<p>A <a href=\"https:\/\/www.clickworker.com\/ai-glossary\/neural-networks\/\">neural network<\/a> is a type of <a href=\"https:\/\/www.clickworker.com\/ai-glossary\/machine-learning\/\">machine learning<\/a> algorithm where the network architecture is made of several node layers. These node layers consist of an input layer, an output layer, and one or more hidden layers. Each node has its own weight and threshold and is connected to another node in the network. Data or output from each node is passed to the next node only if it does not exceed the threshold value.<br>\r\nWhen it satisfies the threshold limitation, the next node is activated and sends data to the next layer of the network. This way, neural networks pass data across each layer depending on the output from the previous layer.<\/p> \r\n\r\n<p><strong>Video on Neural Networks<\/strong><\/p>\r\n<div class=\"row\">\r\n<div class=\"col-12 col-lg-8 mx-auto\">\r\n<div class=\"lyte-wrapper\" title=\"Neural Network In 5 Minutes | What Is A Neural Network? | How Neural Networks Work | Simplilearn\" style=\"width:420px;max-width:100%;margin:5px;\"><div class=\"lyMe\" id=\"WYL_bfmFfD2RIcg\" itemprop=\"video\" itemscope itemtype=\"https:\/\/schema.org\/VideoObject\"><div><meta itemprop=\"thumbnailUrl\" content=\"https:\/\/www.clickworker.com\/wp-content\/plugins\/wp-youtube-lyte\/lyteCache.php?origThumbUrl=https%3A%2F%2Fi.ytimg.com%2Fvi%2FbfmFfD2RIcg%2Fhqdefault.jpg\" \/><meta itemprop=\"embedURL\" content=\"https:\/\/www.youtube.com\/embed\/bfmFfD2RIcg\" \/><meta itemprop=\"duration\" content=\"PT5M45S\" \/><meta itemprop=\"uploadDate\" content=\"2019-06-19T14:30:00Z\" \/><\/div><div id=\"lyte_bfmFfD2RIcg\" data-src=\"https:\/\/www.clickworker.com\/wp-content\/plugins\/wp-youtube-lyte\/lyteCache.php?origThumbUrl=https%3A%2F%2Fi.ytimg.com%2Fvi%2FbfmFfD2RIcg%2Fhqdefault.jpg\" class=\"pL\"><div class=\"tC\"><div class=\"tT\" itemprop=\"name\">Neural Network In 5 Minutes | What Is A Neural Network? | How Neural Networks Work | Simplilearn<\/div><\/div><div class=\"play\"><\/div><div class=\"ctrl\"><div class=\"Lctrl\"><\/div><div class=\"Rctrl\"><\/div><\/div><\/div><noscript><a href=\"https:\/\/youtu.be\/bfmFfD2RIcg\" rel=\"nofollow\"><img decoding=\"async\" src=\"https:\/\/www.clickworker.com\/wp-content\/plugins\/wp-youtube-lyte\/lyteCache.php?origThumbUrl=https%3A%2F%2Fi.ytimg.com%2Fvi%2FbfmFfD2RIcg%2F0.jpg\" alt=\"Neural Network In 5 Minutes | What Is A Neural Network? | How Neural Networks Work | Simplilearn\" width=\"420\" height=\"216\" \/><br \/>Watch this video on YouTube<\/a><\/noscript><meta itemprop=\"description\" content=\"*Note: 1+ Years of Work Experience Recommended to Sign up for Below Programs\u2b07\ufe0f \ud83d\udd25Professional Certificate Course In AI And Machine Learning by IIT Kanpur (India Only): https:\/\/www.simplilearn.com\/iitk-professional-certificate-course-ai-machine-learning?utm_campaign=23AugustTubebuddyExpPCPAIandML&amp;utm_medium=DescriptionFF&amp;utm_source=youtube \ud83d\udd25AI Engineer Masters Program (Discount Code - YTBE15): https:\/\/www.simplilearn.com\/masters-in-artificial-intelligence?utm_campaign=SCE-AIMasters&amp;utm_medium=DescriptionFF&amp;utm_source=youtube \ud83d\udd25AI &amp; Machine Learning Bootcamp(US Only): https:\/\/www.simplilearn.com\/ai-machine-learning-bootcamp?utm_campaign=NNin5Min-bfmFfD2RIcg&amp;utm_medium=DescriptionFF&amp;utm_source=youtube \ud83d\udd25 Purdue Post Graduate Program In AI And Machine Learning: https:\/\/www.simplilearn.com\/pgp-ai-machine-learning-certification-training-course?utm_campaign=NNin5Min-bfmFfD2RIcg&amp;utm_medium=DescriptionFF&amp;utm_source=youtube This video on &quot;What is a Neural Network&quot; delivers an entertaining and exciting introduction to the concepts of Neural Network. We will learn the different layers present in a Neural Network and understand how these layers process data. We will get an idea of the different parameters used in a Neural Network such as weights, bias, and activation functions. We will also understand how to train a Neural Network using forward propagation and then adjust to the errors in the network using the backpropagation method. This video also covers a few popular Neural Network applications. Now, let us jump straight into learning what is a Neural Network. 0:00 What is a Neural Network? 0:33 How Neural Networks work? 03:43 Neural Network examples 04:21 Quiz 04:52 Neural Network applications Don&#039;t forget to take the quiz at 04:21 To learn more about Deep Learning, subscribe to our YouTube channel: https:\/\/www.youtube.com\/user\/Simplilearn?sub_confirmation=1 Download the Artificial Intelligence Career Guide and take a sneak peek into the world that awaits you: https:\/\/www.simplilearn.com\/artificial-intelligence-career-guide-pdf?utm_campaign=NNin5Min-bfmFfD2RIcg&amp;utm_medium=Description&amp;utm_source=youtube Watch more videos on Deep Learning: https:\/\/www.youtube.com\/watch?v=FbxTVRfQFuI&amp;list=PLEiEAq2VkUUIYQ-mMRAGilfOKyWKpHSip #NeuralNetwork #WhatIsANeuralNetwork #WhatAreNeuralNetworks #DeepLearningAndNeuralNetworks #DeepLearning #ArtificalNeuralNetwork #NeuralNetworkExplained #WhatIsDeepLearning #DeepLearningTutorial #DeepLearningCourse #DeepLearningExplained #Simplilearn \u27a1\ufe0f About Caltech Post Graduate Program In AI And Machine Learning Designed to boost your career as an AI and ML professional, this program showcases Caltech CTME&#039;s excellence and IBM&#039;s industry prowess. The artificial intelligence course covers key concepts like Statistics, Data Science with Python, Machine Learning, Deep Learning, NLP, and Reinforcement Learning through an interactive learning model with live sessions. \u2705 Key Features - Simplilearn&#039;s JobAssist helps you get noticed by top hiring companies - PGP AI &amp; ML completion certificate from Caltech CTME - Masterclasses delivered by distinguished Caltech faculty and IBM experts - Caltech CTME Circle Membership - Earn up to 22 CEUs from Caltech CTME - Online convocation by Caltech CTME Program Director - IBM certificates for IBM courses - Access to hackathons and Ask Me Anything sessions from IBM - 25+ hands-on projects from the likes of Twitter, Mercedes Benz, Uber, and many more - Seamless access to integrated labs - Capstone projects in 3 domains - 8X higher interaction in live online classes by industry experts \u2705 Skills Covered - Statistics - Python - Supervised Learning - Unsupervised Learning - Recommendation Systems - NLP - Neural Networks - GANs - Deep Learning - Reinforcement Learning - Speech Recognition - Ensemble Learning - Computer Vision \ud83d\udc49 Learn More At: https:\/\/www.simplilearn.com\/deep-learning-course-with-tensorflow-training?utm_campaign=NNin5Min-bfmFfD2RIcg&amp;utm_medium=Description&amp;utm_source=youtube \ud83d\udd25\ud83d\udd25 Interested in Attending Live Classes? Call Us: IN - 18002127688 \/ US - +18445327688 \ud83c\udf93Enhance your expertise in the below technologies to secure lucrative, high-paying job opportunities: \ud83d\udfe1 AI &amp; Machine Learning - https:\/\/l.linklyhq.com\/l\/1tMt8 \ud83d\udfe2 Cyber Security - https:\/\/l.linklyhq.com\/l\/1tMt9 \ud83d\udd34 Data Analytics - https:\/\/l.linklyhq.com\/l\/1tMt1 \ud83d\udfe0 Data Science - https:\/\/l.linklyhq.com\/l\/1tMt4 \ud83d\udd35 Cloud Computing - https:\/\/l.linklyhq.com\/l\/1tMtA\"><\/div><\/div><div class=\"lL\" style=\"max-width:100%;width:420px;margin:5px;\"><\/div>\r\n<\/div>\r\n<\/div>\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"covnets\">ConvNets \/ CNNs<\/h2>\r\n<p>Convolutional neural networks, also called CNNs or ConvNets, belong to this type of neural network machine learning algorithms where feed-forward networking is used. Before the conception of CNNs, the feature extraction techniques used for <a href=\"https:\/\/www.clickworker.com\/ai-glossary\/computer-vision\/\">computer vision<\/a> tasks were manual and were thus time-consuming. CNNs allow for a more scalable method to feature extraction and several other image recognition tasks, such as image classification and object recognition.<br>\r\nCNN uses mathematical principles such as <a href=\"https:\/\/www.britannica.com\/science\/linear-algebra\" target=\"_blank\" rel=\"noopener\">linear algebra<\/a> and <a href=\"https:\/\/en.wikipedia.org\/wiki\/Matrix_multiplication\" target=\"_blank\" rel=\"noopener\">matrix multiplication<\/a> to recognize patterns in an image. But these tasks done via CNNs can be computationally expensive as they demand high processing power. Thus to work with CNNs, machine learning experts use <a href=\"https:\/\/en.wikipedia.org\/wiki\/Graphics_processing_unit\" target=\"_blank\" rel=\"noopener\">Graphical Processing Units<\/a> (GPUs).<\/p>\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"how\">How does CNN work?<\/h2>\r\n\r\n<p><strong>Video on Convolutional Neural Networks<\/strong><\/p>\r\n<div class=\"row\">\r\n<div class=\"col-12 col-lg-8 mx-auto\">\r\n<div class=\"lyte-wrapper\" title=\"Convolutional Neural Networks (CNNs) explained\" style=\"width:420px;max-width:100%;margin:5px;\"><div class=\"lyMe\" id=\"WYL_YRhxdVk_sIs\" itemprop=\"video\" itemscope itemtype=\"https:\/\/schema.org\/VideoObject\"><div><meta itemprop=\"thumbnailUrl\" content=\"https:\/\/www.clickworker.com\/wp-content\/plugins\/wp-youtube-lyte\/lyteCache.php?origThumbUrl=https%3A%2F%2Fi.ytimg.com%2Fvi%2FYRhxdVk_sIs%2Fhqdefault.jpg\" \/><meta itemprop=\"embedURL\" content=\"https:\/\/www.youtube.com\/embed\/YRhxdVk_sIs\" \/><meta itemprop=\"duration\" content=\"PT8M37S\" \/><meta itemprop=\"uploadDate\" content=\"2017-12-09T21:51:04Z\" \/><\/div><div id=\"lyte_YRhxdVk_sIs\" data-src=\"https:\/\/www.clickworker.com\/wp-content\/plugins\/wp-youtube-lyte\/lyteCache.php?origThumbUrl=https%3A%2F%2Fi.ytimg.com%2Fvi%2FYRhxdVk_sIs%2Fhqdefault.jpg\" class=\"pL\"><div class=\"tC\"><div class=\"tT\" itemprop=\"name\">Convolutional Neural Networks (CNNs) explained<\/div><\/div><div class=\"play\"><\/div><div class=\"ctrl\"><div class=\"Lctrl\"><\/div><div class=\"Rctrl\"><\/div><\/div><\/div><noscript><a href=\"https:\/\/youtu.be\/YRhxdVk_sIs\" rel=\"nofollow\"><img decoding=\"async\" src=\"https:\/\/www.clickworker.com\/wp-content\/plugins\/wp-youtube-lyte\/lyteCache.php?origThumbUrl=https%3A%2F%2Fi.ytimg.com%2Fvi%2FYRhxdVk_sIs%2F0.jpg\" alt=\"Convolutional Neural Networks (CNNs) explained\" width=\"420\" height=\"216\" \/><br \/>Watch this video on YouTube<\/a><\/noscript><meta itemprop=\"description\" content=\"CNNs for deep learning Included in Machine Leaning \/ Deep Learning for Programmers Playlist: https:\/\/www.youtube.com\/playlist?list=PLZbbT5o_s2xq7LwI2y8_QtvuXZedL6tQU Convolution demo on real data: https:\/\/youtu.be\/vJiZqZRkIg8 In this video, we explain the concept of convolutional neural networks, how they&#039;re used, and how they work on a technical level. We also discuss the details behind convolutional layers and filters. fast.ai lesson 4: http:\/\/course17.fast.ai\/lessons\/lesson4.html \ud83d\udd52\ud83e\udd8e VIDEO SECTIONS \ud83e\udd8e\ud83d\udd52 00:00 Welcome to DEEPLIZARD - Go to deeplizard.com for learning resources 00:30 See convolution demo on real data - Link in the description 08:07 Collective Intelligence and the DEEPLIZARD HIVEMIND \ud83d\udca5\ud83e\udd8e DEEPLIZARD COMMUNITY RESOURCES \ud83e\udd8e\ud83d\udca5 \ud83d\udc4b Hey, we&#039;re Chris and Mandy, the creators of deeplizard! \ud83d\udc49 Check out the website for more learning material: \ud83d\udd17 https:\/\/deeplizard.com \ud83d\udcbb ENROLL TO GET DOWNLOAD ACCESS TO CODE FILES \ud83d\udd17 https:\/\/deeplizard.com\/resources \ud83e\udde0 Support collective intelligence, join the deeplizard hivemind: \ud83d\udd17 https:\/\/deeplizard.com\/hivemind \ud83e\udde0 Use code DEEPLIZARD at checkout to receive 15% off your first Neurohacker order \ud83d\udc49 Use your receipt from Neurohacker to get a discount on deeplizard courses \ud83d\udd17 https:\/\/neurohacker.com\/shop?rfsn=6488344.d171c6 \ud83d\udc40 CHECK OUT OUR VLOG: \ud83d\udd17 https:\/\/youtube.com\/deeplizardvlog \u2764\ufe0f\ud83e\udd8e Special thanks to the following polymaths of the deeplizard hivemind: Tammy Mano Prime Ling Li \ud83d\ude80 Boost collective intelligence by sharing this video on social media! \ud83d\udc40 Follow deeplizard: Our vlog: https:\/\/youtube.com\/deeplizardvlog Facebook: https:\/\/facebook.com\/deeplizard Instagram: https:\/\/instagram.com\/deeplizard Twitter: https:\/\/twitter.com\/deeplizard Patreon: https:\/\/patreon.com\/deeplizard YouTube: https:\/\/youtube.com\/deeplizard \ud83c\udf93 Deep Learning with deeplizard: Deep Learning Dictionary - https:\/\/deeplizard.com\/course\/ddcpailzrd Deep Learning Fundamentals - https:\/\/deeplizard.com\/course\/dlcpailzrd Learn TensorFlow - https:\/\/deeplizard.com\/course\/tfcpailzrd Learn PyTorch - https:\/\/deeplizard.com\/course\/ptcpailzrd Natural Language Processing - https:\/\/deeplizard.com\/course\/txtcpailzrd Reinforcement Learning - https:\/\/deeplizard.com\/course\/rlcpailzrd Generative Adversarial Networks - https:\/\/deeplizard.com\/course\/gacpailzrd \ud83c\udf93 Other Courses: DL Fundamentals Classic - https:\/\/deeplizard.com\/learn\/video\/gZmobeGL0Yg Deep Learning Deployment - https:\/\/deeplizard.com\/learn\/video\/SI1hVGvbbZ4 Data Science - https:\/\/deeplizard.com\/learn\/video\/d11chG7Z-xk Trading - https:\/\/deeplizard.com\/learn\/video\/ZpfCK_uHL9Y \ud83d\uded2 Check out products deeplizard recommends on Amazon: \ud83d\udd17 https:\/\/amazon.com\/shop\/deeplizard \ud83c\udfb5 deeplizard uses music by Kevin MacLeod \ud83d\udd17 https:\/\/youtube.com\/channel\/UCSZXFhRIx6b0dFX3xS8L1yQ \u2764\ufe0f Please use the knowledge gained from deeplizard content for good, not evil.\"><\/div><\/div><div class=\"lL\" style=\"max-width:100%;width:420px;margin:5px;\"><\/div>\r\n<\/div>\r\n<\/div>\r\n<p>As mentioned earlier, neural networks are composed of various layers. In the case of CNNs, the layers can be categorized into three major types.<\/p>\r\n<h3>Convolutional layer<\/h3>\r\n<p>The major computational tasks of the CNN occur in the convolutional layer. It is often called the core building block of a convolutional network.<br>\r\nThe basic components of this layer are input data, a filter (or kernel), a feature detector, and a feature map. Image is passed through various receptive fields over multiple iterations and checked to see if a particular feature is present. This process of detecting features in an image is called convolution. Each iteration of applying the filter involves calculating a dot product between the input pixels and the filer.<br>\r\nThe output of this operation is a feature map, a series of dots. Feature maps are also called convolved features. Thus the final output of the convolutional layer is a numerical representation of the image, which is used to identify patterns from the image.<\/p>\r\n\r\n\r\n<h3>Pooling layer<\/h3>\r\n<p>The pooling layer is also called downsampling, as it carries out dimensionality reduction. The feature parameters in the input are reduced to only the necessary parameters to reduce complexity and improve the network\u2019s performance. It also helps avoid the problem of overfitting. There are two types of pooling, namely:<\/p>\r\n<ul><li>Max pooling &#8211; In max pooling, the filer selects the maximum value to be passed to the output<\/li>\r\n<li>Average pooling &#8211; In average pooling, the filter sends an average value from the receptive field to the output array.<\/li><\/ul>\r\n\r\n<h3>Fully connected layer<\/h3>\r\n<p>A fully convoluted (FC) layer is the final layer where the final task of image classification is carried out. Each node in this layer is directly connected to a node in the previous layer. The output of FC layers is usually a probability from 0 to 1 that is derived using a softmax activation function.<br>\r\nThe input first passes through the convolutional layer, followed by one or more pooling layers. The final layer is the fully connected layer which provides the output that identifies the object.<br>\r\n\r\nAs the image data goes through each of these layers, the image pattern or the elements identified progressively get larger until the last layer, where the final image is identified. As such, the data processing gets increasingly complex with each subsequent layer.  <br>\r\n\r\nEach layer tries to identify a particular feature of the input image with the help of a  corresponding filter. The filter or kernel can start as a simple feature and grow increasingly complex with each subsequent layer. Thus the output of each layer is a convolved image that gets increasingly recognized as an object and is passed to the FC layer, where the final image classification takes place. <\/p>\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"history\">History and breakthroughs<\/h2>\r\n<p>While research for image classification has been going on since the 1950s and 1960s, CNNs were first conceptualized in the 1980s. It was researched by Kunihiko Fukushima and Yann LeCun in their work in \u201cNeocognitron: A Self-organizing Neural Network Model for a Mechanism of Pattern RecognitionUnaffected by Shift in Position\u201d (published in 1980)  and in zip code recognition system published in 1989, respectively.<br>\r\n\r\nThe neocognitron invented by Kunihiko Fukushima was based on the discoveries previously made by David Hubel and Torsten Wiesel. Hubel and Wiesel studied the visual system of cats and monkeys and presented their theory that the neurons in the visual cortex of the brains were responsible for visual processing. They were awarded a Nobel Prize in physiology for their findings. These findings inspired Kunihiko to develop his neocognitron which is designed to emulate the visual cortex neurons. Neocognitron was able to recognize basic characters.<br>  \r\n\r\nThe \u201cLeNet5\u201d demonstrated by Yann Lecun in 1994 was a breakthrough in the field of CNNs. It is often regarded as the classic CNN architecture.<br> \r\n\r\nThere have been several variations of the CNN architecture since then, and many advanced techniques have also been incorporated into CNN. Some of the popular CNN architectures are:<\/p>\r\n<ul><li><a href=\"https:\/\/en.wikipedia.org\/wiki\/AlexNet\" target=\"_blank\" rel=\"noopener\">AlexNet<\/a><\/li>\r\n<li><a href=\"https:\/\/medium.com\/analytics-vidhya\/vggnet-architecture-explained-e5c7318aa5b6\" target=\"_blank\" rel=\"noopener\">VGGNet<\/a><\/li> \r\n<li><a href=\"https:\/\/www.mathworks.com\/help\/deeplearning\/ref\/googlenet.html\" target=\"_blank\" rel=\"noopener\">GoogLeNet<\/a><\/li>\r\n<li><a href=\"https:\/\/en.wikipedia.org\/wiki\/Residual_neural_network\" target=\"_blank\" rel=\"noopener\">ResNet<\/a><\/li> \r\n<li><a href=\"https:\/\/towardsdatascience.com\/zfnet-an-explanation-of-paper-with-code-f1bd6752121d\" target=\"_blank\" rel=\"noopener\">ZFNet<\/a><\/li><\/ul> \r\n\r\n<p>The implementation of fast-processing graphics processing units (GPUs) in the 2000s further accelerated the growth of CNNs. In 2004, K.S. Oh and K. Jung implemented 20 times faster CNN with the help of GPU compared to CNNs working with CPUs.<br> \r\n\r\nThe AlexNet developed by researchers from the University of Toronto was another breakthrough with an accuracy of 85% and won the 2012 ImageNet computer vision contest 2012.<br>\r\n\r\nDan Ciresan, in 2019, further accelerated the CNN performance with the help of backpropagation methods.<br>\r\n\r\nBesides GPUs, the less popular Intel Xeon Phi coprocessors are also sometimes used for training CNNs using a parallelization method. Controlled Hogwild with Arbitrary Order of Synchronization (CHAOS) is one such method using the Intel Xeon Phi.<\/p>\r\n\r\n<blockquote class=rounded><p class=\"pb-3\"><strong>Tip:<\/strong><\/p>\r\n<p class=\"pb-3\">Train your Convolutional Neural Network models efficiently by using high quality data that can be provided by clickworker&#8217;s<\/p><a href=\"https:\/\/www.clickworker.com\/ai-datasets-for-machine-learning\/\" class=\"btn btn-primary\" role=\"button\">Datasets for Machine Learning<\/a><\/blockquote>\r\n\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"applications\">Applications and benefits<\/h2>\r\n<p>CNNs find their biggest use in the automated image processing application field, computer vision. Computer vision is an AI-based technology that uses machine learning methods to identify images, videos, and any form of visual input. It is widely used in many facets of everyday life, including marketing, healthcare, retail, automotive, security and surveillance, and more.<br>\r\nBesides image recognition, CNN can also classify audio and signal data. It can be used for time series as well as image data. But the popular use of CNN remains with image recognition tasks, given its high-efficiency level over the other methods.<br>  \r\nThe overall design of a CNN can be compared to how a human brain works. Like neurons in our brain, CNN has several nodes that process data. Just as the frontal lobe of our brain deals with visual input, CNN covers the entire visual field and avoids the problems the previous neural networks faced.<br>\r\nBefore using CNNs, images had to be reduced in resolution and fed as pieces into a neural network. CNN avoids this issue and allows it to process the image. Even audio and speech applications perform better with CNN than traditional neural networks.<br>\r\nTo summarize, here are some of the advantages that CNNs provide over regular neural networks<\/p>\r\n<ul><li>CNN is easy to scale and can process larger and more complex images than traditional neural networks.<\/li>\r\n<li>It avoids the problem of overfitting<\/li>  \r\n<li>It uses parameter sharing where each node connects to another node within a layer and has associated fixed weights\/ This system allows for efficient computation compared to traditional neural networks.<\/li>  \r\n<li>CNN results are more accurate and best suited for Computer Vision systems. <\/li> \r\n<li>It helps avoid manual feature extraction<\/li> \r\n<li>CNNs can be built on top of existing networks and retrained as well. Thus it allows for more scope without increasing computational complexity or the costs involved.<\/li>  \r\n<li>CNN models are easy to deploy and can be run on even smartphones.<\/li><\/ul>  \r\n\r\n<p>Some of the common applications of CNNs and CVs include:<\/p>\r\n<ul><li>Healthcare: Used in diagnostics and anomaly detection in patient data. It is also applied in specific healthcare fields, such as radiology and cancer research<\/li>\r\n<li>Automotive: Used in self-driving cars to process visual inputs<\/li> \r\n<li>Social media: Facial recognition of photos and tagging can be performed with the help of CNNs<\/li> \r\n<li>Retail: Allows for visual search in eCommerce platforms<\/li>\r\n<li>Facial recognition: Law enforcement can make use of facial recognition models such as GANs that are developed based on CNNs<\/li> \r\n<li>Audio processing: Visual assistants can be developed and enhanced with the help of the audio processing capabilities provided by CNNs<\/li> \r\n<li>Drug discovery: AtomNet is a CNN-based deep learning model that helps identify interactions between the molecules in a drug<\/li><\/ul>\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"challenges\">Challenges and concerns<\/h2>\r\n<p>Even though CNNs have proven to be one of the most powerful neural network algorithms available today, they also have their limitations. It is an image recognition AI model but stops being able to recognize patterns and cannot understand the contents of the images. It is yet nowhere near human cognition, which can derive multiple interpretations of an image.<br>\r\nFor instance, an image of a toddler with their father can be interpreted as an image of a middle-aged man and a toddler. But humans can process more, figure out the context and the action, and even perceive the relationship between the two subjects in the image.<br>  \r\nCNN is yet to achieve such a level of intelligence, which can pose problems when applied to practical applications. CNN, thus, cannot be used for content moderation on images in social media. For instance, a CNN model once identified a statue as inappropriate and flagged it for nudity on Facebook.<br>  \r\nThe future of CNN could be that the algorithm is further refined to emulate human intelligence more accurately.<br> \r\nSome of the concerns that a CNN developer will have to take for certain applications are summarized below<\/p>\r\n\r\n<ul><li>Self-driving cars<br>\r\nObject detection for self-driving cars has to be more complex as they should be able to perform localization, obstacle avoidance, and path planning. They should be able to detect incoming collisions and alert the system to take the necessary action. This is a complex problem as CNN must both classify the object and return the position of the bounding box of the object. So the network designer must avoid false alarms and need a huge volume of labeled training data. One source of such labeled data comes from the Google Captcha system, where users are asked to categorize objects like traffic lights, cars, hydrants, and so on.<\/li>\r\n\r\n<li>Text classification<br>\r\nText classification with CNN requires different preprocessing steps compared to image classification. It has also been found that sentences longer than the input matrix width cannot be processed properly.<\/li><\/ul>\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"conclusion\">Conclusion<\/h2>\r\n<p>CNN has proven to be one of the best neural network algorithms to identify images and perform subsequent AI-based operations. AI-based image classification has come a long way, from recognizing just a few characters to identifying many objects quietly enough for autonomous driving. But it has not yet achieved the level of intelligence that can warrant a fully automated intelligent system just as similar to a human brain. While CNN has advanced tech towards such a goal a step closer, the future of CNN could be that the algorithm is further refined to emulate human intelligence more accurately.<\/p>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/section>\r\n\r\n<section class=\"gray section-space\">\r\n\t\t<div class=\"mini-section-spacer\"><\/div>\r\n\t\t<div class=\"mini-section-spacer\"><\/div>\r\n\t\t<div class=\"container\">\t\t\t\r\n\t\t<h2 id=\"faq\">FAQs on Convolutional Neural Network<\/h2>\t\r\n\t\t<!--<div class=\"h2 d-block faq-snippet-heading cw-dark-gray\" data-toggle=\"collapse\" data-target=\"#faqCrowdsourcingSnippet\" role=\"button\" aria-expanded=\"false\" aria-controls=\"faqCrowdsourcingSnippet\">\r\n\t\t\t\tCrowdsourcing - Goolge FAQ Snippets\r\n\t\t\t<\/div>-->\r\n\t\t<div id=\"faqDataAnnotationSnippet\">\r\n\t\t\t\t<div class=\"row\">\r\n\t\t\t\t\t\t<div class=\"col-lg-12\">\r\n\t\t\t\t\t\t\t\t\t<section class=\"sc_fs_faq sc_card card faq-snippet collapsable\">\n\t\t\t<div>\n\t\t\t\t<h2>What is a neural network?<\/h2>\t\t\t\t<div>\n\t\t\t\t\t\t\t\t\t\t<p>\n\t\t\t\t\t\tNeural networks are computer algorithms designed to recognize patterns. They are useful tools to help us classify and cluster data. After they have been correctly trained, they can quickly group data that has not been categorized or labeled, helping save time.\t\t\t\t\t<\/p>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"sc_fs_faq sc_card card faq-snippet collapsable\">\n\t\t\t<div>\n\t\t\t\t<h2>Where are Convolutional Neural Networks used?<\/h2>\t\t\t\t<div>\n\t\t\t\t\t\t\t\t\t\t<p>\n\t\t\t\t\t\tCNNs find their biggest use in the automated image processing application field, computer vision. It is widely used in many facets of everyday life, including marketing, healthcare, retail, automotive, security and surveillance, and more.Besides image recognition, CNN can also classify audio and signal data. It can be used for time series as well as image data. But the popular use of CNN remains with image recognition tasks, given its high-efficiency level over the other methods. \t\t\t\t\t<\/p>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"sc_fs_faq sc_card card faq-snippet collapsable\">\n\t\t\t<div>\n\t\t\t\t<h2>What are CovNets?<\/h2>\t\t\t\t<div>\n\t\t\t\t\t\t\t\t\t\t<p>\n\t\t\t\t\t\tConvolutional neural networks, also called CNNs or ConvNets, belong to this type of neural network machine learning algorithms where feed-forward networking is used.\t\t\t\t\t<\/p>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"sc_fs_faq sc_card card faq-snippet collapsable\">\n\t\t\t<div>\n\t\t\t\t<h2>How many layers are present in CNNs?<\/h2>\t\t\t\t<div>\n\t\t\t\t\t\t\t\t\t\t<p>\n\t\t\t\t\t\tThere are 3 layers- Convolutional layer, Pooling layer and Fully connected layer\t\t\t\t\t<\/p>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/section>\n\t\t\n<script type=\"application\/ld+json\">\n\t{\n\t\t\"@context\": \"https:\/\/schema.org\",\n\t\t\"@type\": \"FAQPage\",\n\t\t\"mainEntity\": [\n\t\t\t\t\t{\n\t\t\t\t\"@type\": \"Question\",\n\t\t\t\t\"name\": \"What is a neural network?\",\n\t\t\t\t\"acceptedAnswer\": {\n\t\t\t\t\t\"@type\": \"Answer\",\n\t\t\t\t\t\"text\": \"Neural networks are computer algorithms designed to recognize patterns. They are useful tools to help us classify and cluster data. After they have been correctly trained, they can quickly group data that has not been categorized or labeled, helping save time.\"\n\t\t\t\t\t\t\t\t\t}\n\t\t\t}\n\t\t\t,\t\t\t\t{\n\t\t\t\t\"@type\": \"Question\",\n\t\t\t\t\"name\": \"Where are Convolutional Neural Networks used?\",\n\t\t\t\t\"acceptedAnswer\": {\n\t\t\t\t\t\"@type\": \"Answer\",\n\t\t\t\t\t\"text\": \"CNNs find their biggest use in the automated image processing application field, computer vision. It is widely used in many facets of everyday life, including marketing, healthcare, retail, automotive, security and surveillance, and more.Besides image recognition, CNN can also classify audio and signal data. It can be used for time series as well as image data. But the popular use of CNN remains with image recognition tasks, given its high-efficiency level over the other methods.\"\n\t\t\t\t\t\t\t\t\t}\n\t\t\t}\n\t\t\t,\t\t\t\t{\n\t\t\t\t\"@type\": \"Question\",\n\t\t\t\t\"name\": \"What are CovNets?\",\n\t\t\t\t\"acceptedAnswer\": {\n\t\t\t\t\t\"@type\": \"Answer\",\n\t\t\t\t\t\"text\": \"Convolutional neural networks, also called CNNs or ConvNets, belong to this type of neural network machine learning algorithms where feed-forward networking is used.\"\n\t\t\t\t\t\t\t\t\t}\n\t\t\t}\n\t\t\t,\t\t\t\t{\n\t\t\t\t\"@type\": \"Question\",\n\t\t\t\t\"name\": \"How many layers are present in CNNs?\",\n\t\t\t\t\"acceptedAnswer\": {\n\t\t\t\t\t\"@type\": \"Answer\",\n\t\t\t\t\t\"text\": \"There are 3 layers- Convolutional layer, Pooling layer and Fully connected layer\"\n\t\t\t\t\t\t\t\t\t}\n\t\t\t}\n\t\t\t\t\t\t]\n\t}\n<\/script>\n\r\n\t\t\t\t\t<\/div>\r\n\t\t\t\t<\/div>\r\n\t\t\t<\/div>\r\n\t\t\t<div class=\"mini-section-spacer\"><\/div>\r\n\t\t\t<div class=\"mini-section-spacer\"><\/div>\r\n\t\t<\/div>\r\n\r\n\t<\/section>","protected":false},"excerpt":{"rendered":"<p>C Convolutional Neural Network All About Convolutional Neural Networks What are Convolutional Neural Networks? What is a neural network? Introduction to ConvNets \/ CNNs How does CNN work? History and breakthroughs Applications and benefits Challenges and concerns Conclusion FAQs What are Convolutional Neural Networks? The convolutional neural network, called CNN, is a deep learning algorithm [&hellip;]<\/p>\n","protected":false},"author":65,"featured_media":0,"parent":54483,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"blank_with_breadcrumb.php","meta":{"footnotes":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Convolutional Neural Network - AI glossary \/ term explanation<\/title>\n<meta name=\"description\" content=\"Convolutional Neural Network is a deep learning algorithm for image recognition tasks.\" \/>\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.clickworker.com\/ai-glossary\/convolutional-neural-network\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Convolutional Neural Network - 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