Which of the following is not true about deep learning?<\/p>\n\n\n\n
It is a subset of machine learning:<\/p>\n\n\n\n
It is also known as supervised learning.<\/p>\n\n\n\n
It does not require a huge set of training data.<\/p>\n\n\n\n
It learns from mistakes.<\/p>\n\n\n\n
The correct answer and explanation is :<\/strong><\/mark><\/p>\n\n\n\n Correct Answer: It does not require a huge set of training data.<\/strong><\/p>\n\n\n\n Deep learning is a powerful subfield of machine learning that utilizes artificial neural networks to model and solve complex problems. Let\u2019s evaluate the truthfulness of each statement:<\/p>\n\n\n\n 1. “It is a subset of machine learning”:<\/strong> 2. “It is also known as supervised learning”:<\/strong> 3. “It does not require a huge set of training data”:<\/strong> 4. “It learns from mistakes”:<\/strong> In summary, the incorrect statement<\/strong> is: Which of the following is not true about deep learning? It is a subset of machine learning: It is also known as supervised learning. It does not require a huge set of training data. It learns from mistakes. The correct answer and explanation is : Correct Answer: It does not require a huge set of […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[25],"tags":[],"class_list":["post-207767","post","type-post","status-publish","format-standard","hentry","category-exams-certification"],"_links":{"self":[{"href":"https:\/\/www.learnexams.com\/blog\/wp-json\/wp\/v2\/posts\/207767","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.learnexams.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.learnexams.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.learnexams.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.learnexams.com\/blog\/wp-json\/wp\/v2\/comments?post=207767"}],"version-history":[{"count":0,"href":"https:\/\/www.learnexams.com\/blog\/wp-json\/wp\/v2\/posts\/207767\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.learnexams.com\/blog\/wp-json\/wp\/v2\/media?parent=207767"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.learnexams.com\/blog\/wp-json\/wp\/v2\/categories?post=207767"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.learnexams.com\/blog\/wp-json\/wp\/v2\/tags?post=207767"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Explanation:<\/h3>\n\n\n\n
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\u2705 True.<\/strong>
Deep learning is indeed a subset of machine learning. While machine learning encompasses a wide range of techniques (e.g., decision trees, support vector machines, etc.), deep learning specifically refers to algorithms based on artificial neural networks, particularly those with multiple (deep) layers.<\/p>\n\n\n\n
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\u2705 Partially true.<\/strong>
While deep learning can be used in supervised learning<\/strong>, it is not exclusively supervised<\/strong>. Deep learning models can also be applied in unsupervised learning<\/strong> (e.g., autoencoders, GANs) and reinforcement learning<\/strong>. However, many of the most common applications of deep learning (like image classification and speech recognition) use supervised learning, which is why this statement is not entirely incorrect\u2014but it’s overly broad.<\/p>\n\n\n\n
\n\n\n\n
\u274c False (Correct Answer).<\/strong>
This statement is not true. Deep learning models generally require large amounts of data<\/strong> to perform well. The reason is that these models have millions of parameters<\/strong>, which need to be adjusted during training. Without sufficient data, the model can overfit<\/strong>\u2014performing well on training data but poorly on new, unseen data. High data volume allows the network to generalize better and learn more accurate representations.<\/p>\n\n\n\n
\n\n\n\n
\u2705 True.<\/strong>
This is a core concept in deep learning. During training, the model makes predictions, compares them to the actual results, and calculates a loss (or error). Using a process called backpropagation<\/strong>, the model adjusts its internal parameters to reduce this loss over time\u2014essentially learning from its mistakes.<\/p>\n\n\n\n
\n\n\n\n
\u201cIt does not require a huge set of training data.\u201d<\/strong>
Deep learning thrives on large datasets and often underperforms when data is limited.<\/p>\n","protected":false},"excerpt":{"rendered":"