🕷️ Crawler Inspector

URL Lookup

Direct Parameter Lookup

Raw Queries and Responses

1. Shard Calculation

Query:

Response:

Calculated Shard: 103 (from laksa024)

2. Crawled Status Check

Query:

curl -X POST \
  'http://laksa103.int.ahrefs:8124/' \
  -H 'Content-Type: text/plain' \
  -H 'X-ClickHouse-Database: crawler3' \
  -H 'Authorization: Basic YXBpOg==' \
  -d 'SELECT getAhrefsURLFromUnparsed(src_unparsed) AS found_url, ifNull(toUnixTimestamp(download_stamp), 0) AS crawl_time, ifNull(toUnixTimestamp(props_url_first_seen), 0) AS first_indexed_time, download_http_code AS http_code, src_unparsed AS src_unparsed, src_root_hash AS src_root_hash, history_drop_reason AS history_drop_reason, meta_title AS meta_title, meta_descriptions AS meta_descriptions, attrs_boilerpipe_text AS attrs_boilerpipe_text, attrs_markdown AS attrs_markdown, attrs_readable_markdown AS attrs_readable_markdown, meta_canonical AS meta_canonical FROM crawler3.page_info_local FINAL PREWHERE (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://www.geeksforgeeks.org/deep-learning/convolutional-neural-network-cnn-in-machine-learning/\')), getAhrefsUnparsedNoserviceFromURL(\'https://www.geeksforgeeks.org/deep-learning/convolutional-neural-network-cnn-in-machine-learning/\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/www.geeksforgeeks.org\/deep-learning\/convolutional-neural-network-cnn-in-machine-learning\/","crawl_time":1775857584,"first_indexed_time":1750033375,"http_code":200,"src_unparsed":"org,geeksforgeeks!www,\/deep-learning\/convolutional-neural-network-cnn-in-machine-learning\/ s443","src_root_hash":"12046344915360636903","history_drop_reason":null,"meta_title":"Convolutional Neural Network (CNN) in Deep Learning - GeeksforGeeks","meta_descriptions":["Your All-in-One Learning Portal: GeeksforGeeks is a comprehensive educational platform that empowers learners across domains-spanning computer science and programming, school education, upskilling, commerce, software tools, competitive exams, and more.","Your All-in-One Learning Portal. It contains well written, well thought and well explained computer science and programming articles, quizzes and practice\/competitive programming\/company interview Questions."],"attrs_boilerpipe_text":"Convolutional Neural Network (CNN) in Deep Learning\nSuggested Quiz\n6 Questions\nWhat is the primary function of convolutional layers in a CNN?\nA\nTo reduce the size of the image\nB\nTo detect features like edges and textures\nC\nTo classify images\nD\nTo add non-linearity\nWhat is the role of activation functions in CNNs?\nA\nTo normalize data\nB\nTo introduce non-linearity\nC\nTo reduce computational complexity\nD\nTo generate labels\nWhich CNN architecture first won the ImageNet competition in 2012?\nA\nLeNet\nB\nAlexNet\nC\nVGG\nD\nResNet\nWhat is the key innovation introduced by ResNet architecture?\nA\nInception modules\nB\nSkip connections\nC\nDropout layers\nD\nFully connected layers\nWhat type of problem is typically solved using CNNs in medical imaging?\nA\nDisease detection\nB\nSentiment analysis\nC\nTime series prediction\nD\nLanguage translation\nWhich CNN model is known for using small 3x3 convolution filters with deep layers?\nA\nGoogleNet\nB\nAlexNet\nC\nVGG\nD\nResNet\nQuiz Completed Successfully\nYour Score :\n0\n\/\n6\nAccuracy :\n0%\nLogin to View Explanation\n1\n\/6","attrs_markdown":"[![geeksforgeeks](https:\/\/media.geeksforgeeks.org\/gfg-gg-logo.svg)](https:\/\/www.geeksforgeeks.org\/)\n\n![search icon](https:\/\/media.geeksforgeeks.org\/auth-dashboard-uploads\/Property=Light---Default.svg)\n\n- Sign In\n- [Courses]()\n- [Tutorials]()\n- [Interview Prep]()\n\n- [DSA](https:\/\/www.geeksforgeeks.org\/dsa\/dsa-tutorial-learn-data-structures-and-algorithms\/)\n- [Practice Problems](https:\/\/www.geeksforgeeks.org\/explore)\n- [C](https:\/\/www.geeksforgeeks.org\/c\/c-programming-language\/)\n- [C++](https:\/\/www.geeksforgeeks.org\/cpp\/c-plus-plus\/)\n- [Java](https:\/\/www.geeksforgeeks.org\/java\/java\/)\n- [Python](https:\/\/www.geeksforgeeks.org\/python\/python-programming-language-tutorial\/)\n- [JavaScript](https:\/\/www.geeksforgeeks.org\/javascript\/javascript-tutorial\/)\n- [Data Science](https:\/\/www.geeksforgeeks.org\/data-science\/data-science-for-beginners\/)\n- [Machine Learning](https:\/\/www.geeksforgeeks.org\/machine-learning\/machine-learning\/)\n- [Courses](https:\/\/www.geeksforgeeks.org\/courses)\n\n# Convolutional Neural Network (CNN) in Deep Learning\nLast Updated : 30 Jan, 2026\n\n[Convolutional Neural Networks (CNNs)](https:\/\/www.geeksforgeeks.org\/machine-learning\/introduction-convolution-neural-network\/) are deep learning models designed to process data with a grid-like topology such as images. They are the foundation for most modern computer vision applications to detect features within visual data.\n\n![Introduction-to-Convolutional-Neural-Networks\\_.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20250529121802768308\/Introduction-to-Convolutional-Neural-Networks_.webp)![Introduction-to-Convolutional-Neural-Networks\\_.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20250529121802768308\/Introduction-to-Convolutional-Neural-Networks_.webp)\n\n![Convolutional-Neural-Network-in-Machine-Learning.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20250529121802516451\/Convolutional-Neural-Network-in-Machine-Learning.webp)![Convolutional-Neural-Network-in-Machine-Learning.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20250529121802516451\/Convolutional-Neural-Network-in-Machine-Learning.webp)\n\n![Application-of-CNN.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20250529121801792647\/Application-of-CNN.webp)![Application-of-CNN.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20250529121801792647\/Application-of-CNN.webp)\n\n2 \/ 3\n### Key Components\n- [****Convolutional Layers****](https:\/\/www.geeksforgeeks.org\/machine-learning\/what-are-convolution-layers\/)****:**** These layers apply convolutional operations to input images using filters or kernels to detect features such as edges, textures and more complex patterns. Convolutional operations help preserve the spatial relationships between pixels.\n- [****Pooling Layers****](https:\/\/www.geeksforgeeks.org\/deep-learning\/cnn-introduction-to-pooling-layer\/)****:**** They downsample the spatial dimensions of the input, reducing the computational complexity and the number of parameters in the network. Max pooling is a common pooling operation where we select a maximum value from a group of neighboring pixels.\n- [****Activation Functions****](https:\/\/www.geeksforgeeks.org\/machine-learning\/activation-functions-neural-networks\/)****:**** They introduce non-linearity to the model by allowing it to learn more complex relationships in the data.\n- [****Fully Connected Layers****](https:\/\/www.geeksforgeeks.org\/deep-learning\/what-is-fully-connected-layer-in-deep-learning\/)****:**** These layers are responsible for making predictions based on the high-level features learned by the previous layers. They connect every neuron in one layer to every neuron in the next layer.\n## Working of CNN\n1. ****Input Image****: CNN receives an input image which is preprocessed to ensure uniformity in size and format.\n2. ****Convolutional Layers****: Filters are applied to the input image to extract features like edges, textures and shapes.\n3. ****Pooling Layers****: The feature maps generated by the convolutional layers are downsampled to reduce dimensionality.\n4. ****Fully Connected Layers****: The downsampled feature maps are passed through fully connected layers to produce the final output, such as a classification label.\n5. ****Output****: The CNN outputs a prediction, such as the class of the image.\n\n![Working-of-CNN\\_](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20250207123959732912\/Working-of-CNN_.webp)\n\nWorking of CNN Models\n## ****Training a Convolutional Neural Network****\nCNNs are trained using a supervised learning approach. This means that the CNN is given a set of labeled training images. The CNN learns to map the input images to their correct labels.\n\nThe training process for a CNN involves the following steps:\n\n1. ****Data Preparation:**** The training images are preprocessed to ensure that they are all in the same format and size.\n2. ****Loss Function:**** A loss function is used to measure how well the CNN is performing on the training data. The loss function is typically calculated by taking the difference between the predicted labels and the actual labels of the training images.\n3. ****Optimizer:**** An optimizer is used to update the weights of the CNN in order to minimize the loss function.\n4. ****Backpropagation:**** Backpropagation is a technique used to calculate the gradients of the loss function with respect to the weights of the CNN. The gradients are then used to update the weights of the CNN using the optimizer.\n## ****How to Evaluate CNN Models****\nEfficiency of CNN can be evaluated using a variety of criteria. Among the most popular metrics are:\n\n- ****Accuracy:**** Accuracy is the percentage of test images that the CNN correctly classifies.\n- ****Precision:**** Precision is the percentage of test images that the CNN predicts as a particular class and that are actually of that class.\n- ****Recall:**** Recall is the percentage of test images that are of a particular class and that the CNN predicts as that class.\n- ****F1 Score:**** The F1 Score is a harmonic mean of precision and recall. It is a good metric for evaluating the performance of a CNN on classes that are imbalanced.\n## ****Different Types of CNN Models****\n****1\\. LeNet:**** [LeNet](https:\/\/www.geeksforgeeks.org\/computer-vision\/what-is-lenet\/) developed by Yann LeCun and his colleagues in the late 1990s was one of the first successful CNNs designed for handwritten digit recognition. It laid the foundation for modern CNNs and achieved high accuracy on the MNIST dataset which contains 70,000 images of handwritten digits (0-9).\n\n****2\\. AlexNet:**** [AlexNet](https:\/\/www.geeksforgeeks.org\/machine-learning\/ml-getting-started-with-alexnet\/) is a CNN architecture that was developed by Alex Krizhevsky, Ilya Sutskever and Geoffrey Hinton in 2012. It was the first CNN to win the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) a major image recognition competition. It consists of several layers of convolutional and pooling layers followed by fully connected layers. The architecture includes five convolutional layers, three pooling layers and three fully connected layers.\n\n****3\\. Resnet:**** [ResNets (Residual Networks)](https:\/\/www.geeksforgeeks.org\/deep-learning\/residual-networks-resnet-deep-learning\/) are designed for image recognition and processing tasks. They are renowned for their ability to train very deep networks without overfitting making them highly effective for complex tasks. It introduces skip connections that allow the network to learn residual functions making it easier to train deep architecture.\n\n4\\. ****GoogleNet:**** [GoogleNet](https:\/\/www.geeksforgeeks.org\/machine-learning\/understanding-googlenet-model-cnn-architecture\/) also known as InceptionNet is renowned for achieving high accuracy in image classification while using fewer parameters and computational resources compared to other state-of-the-art CNNs. The core component of GoogleNet allows the network to learn features at different scales simultaneously to enhance performance.\n\n****5\\. VGG:**** [VGGs](https:\/\/www.geeksforgeeks.org\/computer-vision\/vgg-net-architecture-explained\/) are developed by the Visual Geometry Group at Oxford, it uses small 3x3 convolutional filters stacked in multiple layers, creating a deep and uniform structure. Popular variants like VGG-16 and VGG-19 achieved state-of-the-art performance on the ImageNet dataset demonstrating the power of depth in CNNs.\n\n## ****Applications****\n- ****Image classification:**** CNNs are the state-of-the-art models for image classification. They can be used to classify images into different categories such as cats and dogs.\n- ****Object detection:**** It can be used to detect objects in images such as people, cars and buildings. They can also be used to localize objects in images which means that they can identify the location of an object in an image.\n- ****Image segmentation:**** It can be used to segment images which means that they can identify and label different objects in an image. This is useful for applications such as medical imaging and robotics.\n- ****Video analysis:**** It can be used to analyze videos such as tracking objects in a video or detecting events in a video. This is useful for applications such as video surveillance and traffic monitoring.\n## ****Advantages****\n- ****High Accuracy****: They can achieve high accuracy in various image recognition tasks.\n- ****Efficiency****: They are efficient, especially when implemented on GPUs.\n- ****Robustness****: They are robust to noise and variations in input data.\n- ****Adaptability****: It can be adapted to different tasks by modifying their architecture.\n## ****Disadvantages****\n- ****Complexity****: It can be complex and difficult to train, especially for large datasets.\n- ****Resource-Intensive****: It require significant computational resources for training and deployment.\n- ****Data Requirements****: They need large amounts of labeled data for training.\n- ****Interpretability****: They can be difficult to interpret making it challenging to understand their predictions.\n\nConvolutional Neural Network (CNN) in Deep Learning\n\nSuggested Quiz\n\n![reset](https:\/\/media.geeksforgeeks.org\/auth-dashboard-uploads\/Reset-icon---Light.svg)\n\n6 Questions\n\nWhat is the primary function of convolutional layers in a CNN?\n\n- A\n  \n  To reduce the size of the image\n- B\n  \n  To detect features like edges and textures\n- C\n  \n  To classify images\n- D\n  \n  To add non-linearity\n\nWhat is the role of activation functions in CNNs?\n\n- A\n  \n  To normalize data\n- B\n  \n  To introduce non-linearity\n- C\n  \n  To reduce computational complexity\n- D\n  \n  To generate labels\n\nWhich CNN architecture first won the ImageNet competition in 2012?\n\n- A\n  \n  LeNet\n- B\n  \n  AlexNet\n- C\n  \n  VGG\n- D\n  \n  ResNet\n\nWhat is the key innovation introduced by ResNet architecture?\n\n- A\n  \n  Inception modules\n- B\n  \n  Skip connections\n- C\n  \n  Dropout layers\n- D\n  \n  Fully connected layers\n\nWhat type of problem is typically solved using CNNs in medical imaging?\n\n- A\n  \n  Disease detection\n- B\n  \n  Sentiment analysis\n- C\n  \n  Time series prediction\n- D\n  \n  Language translation\n\nWhich CNN model is known for using small 3x3 convolution filters with deep layers?\n\n- A\n  \n  GoogleNet\n- B\n  \n  AlexNet\n- C\n  \n  VGG\n- D\n  \n  ResNet\n\n![success](https:\/\/media.geeksforgeeks.org\/auth-dashboard-uploads\/sucess-img.png)\n\nQuiz Completed Successfully\n\nYour Score :0\/6\n\nAccuracy :0%\n\nLogin to View Explanation\n\n**1**\/6\n\n\\< Previous\n\nNext \\>\n\nComment\n\n[G](https:\/\/www.geeksforgeeks.org\/user\/goelaparna1520\/)\n\n[goelaparna1520](https:\/\/www.geeksforgeeks.org\/user\/goelaparna1520\/)\n\n52\n\nArticle Tags:\n\nArticle Tags:\n\n[Deep Learning](https:\/\/www.geeksforgeeks.org\/category\/ai-ml-ds\/deep-learning\/)\n\n[AI-ML-DS](https:\/\/www.geeksforgeeks.org\/category\/ai-ml-ds\/)\n\n[Neural Network](https:\/\/www.geeksforgeeks.org\/tag\/neural-network\/)\n### Explore\n\n[![GeeksforGeeks](https:\/\/media.geeksforgeeks.org\/auth-dashboard-uploads\/gfgFooterLogo.png)](https:\/\/www.geeksforgeeks.org\/)\n\n![location](https:\/\/media.geeksforgeeks.org\/img-practice\/Location-1685004904.svg)\n\nCorporate & Communications Address:\n\nA-143, 7th Floor, Sovereign Corporate Tower, Sector- 136, Noida, Uttar Pradesh (201305)\n\n![location](https:\/\/media.geeksforgeeks.org\/img-practice\/Location-1685004904.svg)\n\nRegistered Address:\n\nK 061, Tower K, Gulshan Vivante Apartment, Sector 137, Noida, Gautam Buddh Nagar, Uttar Pradesh, 201305\n\n[![GFG App on Play Store](https:\/\/media.geeksforgeeks.org\/auth-dashboard-uploads\/googleplay-%281%29.png)](https:\/\/geeksforgeeksapp.page.link\/gfg-app)[![GFG App on App Store](https:\/\/media.geeksforgeeks.org\/auth-dashboard-uploads\/appstore-%281%29.png)](https:\/\/geeksforgeeksapp.page.link\/gfg-app)\n\n- Company\n- [About Us](https:\/\/www.geeksforgeeks.org\/about\/)\n- [Legal](https:\/\/www.geeksforgeeks.org\/legal\/)\n- [Privacy Policy](https:\/\/www.geeksforgeeks.org\/legal\/privacy-policy\/)\n- [Contact Us](https:\/\/www.geeksforgeeks.org\/about\/contact-us\/)\n- [Advertise with us](https:\/\/www.geeksforgeeks.org\/advertise-with-us\/)\n- [GFG Corporate Solution](https:\/\/www.geeksforgeeks.org\/gfg-corporate-solution\/)\n- [Campus Training Program](https:\/\/www.geeksforgeeks.org\/campus-training-program\/)\n\n- Explore\n- [POTD](https:\/\/www.geeksforgeeks.org\/problem-of-the-day)\n- [Job-A-Thon](https:\/\/practice.geeksforgeeks.org\/events\/rec\/job-a-thon\/)\n- [Blogs](https:\/\/www.geeksforgeeks.org\/category\/blogs\/?type=recent)\n- [Nation Skill Up](https:\/\/www.geeksforgeeks.org\/nation-skill-up\/)\n\n- Tutorials\n- [Programming Languages](https:\/\/www.geeksforgeeks.org\/computer-science-fundamentals\/programming-language-tutorials\/)\n- [DSA](https:\/\/www.geeksforgeeks.org\/dsa\/dsa-tutorial-learn-data-structures-and-algorithms\/)\n- [Web Technology](https:\/\/www.geeksforgeeks.org\/web-tech\/web-technology\/)\n- [AI, ML & Data Science](https:\/\/www.geeksforgeeks.org\/machine-learning\/ai-ml-and-data-science-tutorial-learn-ai-ml-and-data-science\/)\n- [DevOps](https:\/\/www.geeksforgeeks.org\/devops\/devops-tutorial\/)\n- [CS Core Subjects](https:\/\/www.geeksforgeeks.org\/gate\/gate-exam-tutorial\/)\n- [Interview Preparation](https:\/\/www.geeksforgeeks.org\/aptitude\/interview-corner\/)\n- [Software and Tools](https:\/\/www.geeksforgeeks.org\/websites-apps\/software-and-tools-a-to-z-list\/)\n\n- Courses\n- [ML and Data Science](https:\/\/www.geeksforgeeks.org\/courses\/category\/machine-learning-data-science)\n- [DSA and Placements](https:\/\/www.geeksforgeeks.org\/courses\/category\/dsa-placements)\n- [Web Development](https:\/\/www.geeksforgeeks.org\/courses\/category\/development-testing)\n- [Programming Languages](https:\/\/www.geeksforgeeks.org\/courses\/category\/programming-languages)\n- [DevOps & Cloud](https:\/\/www.geeksforgeeks.org\/courses\/category\/cloud-devops)\n- [GATE](https:\/\/www.geeksforgeeks.org\/courses\/category\/gate)\n- [Trending Technologies](https:\/\/www.geeksforgeeks.org\/courses\/category\/trending-technologies\/)\n\n- Videos\n- [DSA](https:\/\/www.geeksforgeeks.org\/videos\/category\/sde-sheet\/)\n- [Python](https:\/\/www.geeksforgeeks.org\/videos\/category\/python\/)\n- [Java](https:\/\/www.geeksforgeeks.org\/videos\/category\/java-w6y5f4\/)\n- [C++](https:\/\/www.geeksforgeeks.org\/videos\/category\/c\/)\n- [Web Development](https:\/\/www.geeksforgeeks.org\/videos\/category\/web-development\/)\n- [Data Science](https:\/\/www.geeksforgeeks.org\/videos\/category\/data-science\/)\n- [CS Subjects](https:\/\/www.geeksforgeeks.org\/videos\/category\/cs-subjects\/)\n\n- Preparation Corner\n- [Interview Corner](https:\/\/www.geeksforgeeks.org\/interview-prep\/interview-corner\/)\n- [Aptitude](https:\/\/www.geeksforgeeks.org\/aptitude\/aptitude-questions-and-answers\/)\n- [Puzzles](https:\/\/www.geeksforgeeks.org\/aptitude\/puzzles\/)\n- [GfG 160](https:\/\/www.geeksforgeeks.org\/courses\/gfg-160-series)\n- [System Design](https:\/\/www.geeksforgeeks.org\/system-design\/system-design-tutorial\/)\n\n[@GeeksforGeeks, Sanchhaya Education Private Limited](https:\/\/www.geeksforgeeks.org\/), [All rights reserved](https:\/\/www.geeksforgeeks.org\/copyright-information\/)\n\n![]()","attrs_readable_markdown":"Convolutional Neural Network (CNN) in Deep Learning\n\nSuggested Quiz\n\n6 Questions\n\nWhat is the primary function of convolutional layers in a CNN?\n\n- A\n  \n  To reduce the size of the image\n- B\n  \n  To detect features like edges and textures\n- C\n  \n  To classify images\n- D\n  \n  To add non-linearity\n\nWhat is the role of activation functions in CNNs?\n\n- A\n  \n  To normalize data\n- B\n  \n  To introduce non-linearity\n- C\n  \n  To reduce computational complexity\n- D\n  \n  To generate labels\n\nWhich CNN architecture first won the ImageNet competition in 2012?\n\n- A\n  \n  LeNet\n- B\n  \n  AlexNet\n- C\n  \n  VGG\n- D\n  \n  ResNet\n\nWhat is the key innovation introduced by ResNet architecture?\n\n- A\n  \n  Inception modules\n- B\n  \n  Skip connections\n- C\n  \n  Dropout layers\n- D\n  \n  Fully connected layers\n\nWhat type of problem is typically solved using CNNs in medical imaging?\n\n- A\n  \n  Disease detection\n- B\n  \n  Sentiment analysis\n- C\n  \n  Time series prediction\n- D\n  \n  Language translation\n\nWhich CNN model is known for using small 3x3 convolution filters with deep layers?\n\n- A\n  \n  GoogleNet\n- B\n  \n  AlexNet\n- C\n  \n  VGG\n- D\n  \n  ResNet\n\n![success](https:\/\/media.geeksforgeeks.org\/auth-dashboard-uploads\/sucess-img.png)\n\nQuiz Completed Successfully\n\nYour Score :0\/6\n\nAccuracy :0%\n\nLogin to View Explanation\n\n**1**\/6","meta_canonical":null}

3. Robots.txt Check

Query:

Response:

4. Spam/Ban Check

Query:

Response:

5. Seen Status Check

ℹ️ Skipped - page is already crawled

📄

INDEXABLE

✅

CRAWLED

1 day ago

🤖

ROBOTS ALLOWED

Page Info Filters

Filter	Status	Condition	Details
HTTP status	PASS	`download_http_code = 200`	HTTP 200
Age cutoff	PASS	`download_stamp > now() - 6 MONTH`	0 months ago
History drop	PASS	`isNull(history_drop_reason)`	No drop reason
Spam/ban	PASS	`fh_dont_index != 1 AND ml_spam_score = 0`	ml_spam_score=0
Canonical	PASS	`meta_canonical IS NULL OR = '' OR = src_unparsed`	Not set

Page Details

Property	Value
URL	https://www.geeksforgeeks.org/deep-learning/convolutional-neural-network-cnn-in-machine-learning/
Last Crawled	2026-04-10 21:46:24 (1 day ago)
First Indexed	2025-06-16 00:22:55 (10 months ago)
HTTP Status Code	200
Meta Title	Convolutional Neural Network (CNN) in Deep Learning - GeeksforGeeks
Meta Description	Your All-in-One Learning Portal: GeeksforGeeks is a comprehensive educational platform that empowers learners across domains-spanning computer science and programming, school education, upskilling, commerce, software tools, competitive exams, and more., Your All-in-One Learning Portal. It contains well written, well thought and well explained computer science and programming articles, quizzes and practice/competitive programming/company interview Questions.
Meta Canonical	null
Boilerpipe Text	Convolutional Neural Network (CNN) in Deep Learning Suggested Quiz 6 Questions What is the primary function of convolutional layers in a CNN? A To reduce the size of the image B To detect features like edges and textures C To classify images D To add non-linearity What is the role of activation functions in CNNs? A To normalize data B To introduce non-linearity C To reduce computational complexity D To generate labels Which CNN architecture first won the ImageNet competition in 2012? A LeNet B AlexNet C VGG D ResNet What is the key innovation introduced by ResNet architecture? A Inception modules B Skip connections C Dropout layers D Fully connected layers What type of problem is typically solved using CNNs in medical imaging? A Disease detection B Sentiment analysis C Time series prediction D Language translation Which CNN model is known for using small 3x3 convolution filters with deep layers? A GoogleNet B AlexNet C VGG D ResNet Quiz Completed Successfully Your Score : 0 / 6 Accuracy : 0% Login to View Explanation 1 /6
Markdown	[![geeksforgeeks](https://media.geeksforgeeks.org/gfg-gg-logo.svg)](https://www.geeksforgeeks.org/) ![search icon](https://media.geeksforgeeks.org/auth-dashboard-uploads/Property=Light---Default.svg) - Sign In - [Courses]() - [Tutorials]() - [Interview Prep]() - [DSA](https://www.geeksforgeeks.org/dsa/dsa-tutorial-learn-data-structures-and-algorithms/) - [Practice Problems](https://www.geeksforgeeks.org/explore) - [C](https://www.geeksforgeeks.org/c/c-programming-language/) - [C++](https://www.geeksforgeeks.org/cpp/c-plus-plus/) - [Java](https://www.geeksforgeeks.org/java/java/) - [Python](https://www.geeksforgeeks.org/python/python-programming-language-tutorial/) - [JavaScript](https://www.geeksforgeeks.org/javascript/javascript-tutorial/) - [Data Science](https://www.geeksforgeeks.org/data-science/data-science-for-beginners/) - [Machine Learning](https://www.geeksforgeeks.org/machine-learning/machine-learning/) - [Courses](https://www.geeksforgeeks.org/courses) # Convolutional Neural Network (CNN) in Deep Learning Last Updated : 30 Jan, 2026 [Convolutional Neural Networks (CNNs)](https://www.geeksforgeeks.org/machine-learning/introduction-convolution-neural-network/) are deep learning models designed to process data with a grid-like topology such as images. They are the foundation for most modern computer vision applications to detect features within visual data. ![Introduction-to-Convolutional-Neural-Networks\_.webp](https://media.geeksforgeeks.org/wp-content/uploads/20250529121802768308/Introduction-to-Convolutional-Neural-Networks_.webp)![Introduction-to-Convolutional-Neural-Networks\_.webp](https://media.geeksforgeeks.org/wp-content/uploads/20250529121802768308/Introduction-to-Convolutional-Neural-Networks_.webp) ![Convolutional-Neural-Network-in-Machine-Learning.webp](https://media.geeksforgeeks.org/wp-content/uploads/20250529121802516451/Convolutional-Neural-Network-in-Machine-Learning.webp)![Convolutional-Neural-Network-in-Machine-Learning.webp](https://media.geeksforgeeks.org/wp-content/uploads/20250529121802516451/Convolutional-Neural-Network-in-Machine-Learning.webp) ![Application-of-CNN.webp](https://media.geeksforgeeks.org/wp-content/uploads/20250529121801792647/Application-of-CNN.webp)![Application-of-CNN.webp](https://media.geeksforgeeks.org/wp-content/uploads/20250529121801792647/Application-of-CNN.webp) 2 / 3 ### Key Components - [**Convolutional Layers](https://www.geeksforgeeks.org/machine-learning/what-are-convolution-layers/): These layers apply convolutional operations to input images using filters or kernels to detect features such as edges, textures and more complex patterns. Convolutional operations help preserve the spatial relationships between pixels. - [Pooling Layers](https://www.geeksforgeeks.org/deep-learning/cnn-introduction-to-pooling-layer/): They downsample the spatial dimensions of the input, reducing the computational complexity and the number of parameters in the network. Max pooling is a common pooling operation where we select a maximum value from a group of neighboring pixels. - [Activation Functions](https://www.geeksforgeeks.org/machine-learning/activation-functions-neural-networks/): They introduce non-linearity to the model by allowing it to learn more complex relationships in the data. - [Fully Connected Layers](https://www.geeksforgeeks.org/deep-learning/what-is-fully-connected-layer-in-deep-learning/): These layers are responsible for making predictions based on the high-level features learned by the previous layers. They connect every neuron in one layer to every neuron in the next layer. ## Working of CNN 1. Input Image: CNN receives an input image which is preprocessed to ensure uniformity in size and format. 2. Convolutional Layers: Filters are applied to the input image to extract features like edges, textures and shapes. 3. Pooling Layers: The feature maps generated by the convolutional layers are downsampled to reduce dimensionality. 4. Fully Connected Layers: The downsampled feature maps are passed through fully connected layers to produce the final output, such as a classification label. 5. Output: The CNN outputs a prediction, such as the class of the image. ![Working-of-CNN\_](https://media.geeksforgeeks.org/wp-content/uploads/20250207123959732912/Working-of-CNN_.webp) Working of CNN Models ## Training a Convolutional Neural Network CNNs are trained using a supervised learning approach. This means that the CNN is given a set of labeled training images. The CNN learns to map the input images to their correct labels. The training process for a CNN involves the following steps: 1. Data Preparation: The training images are preprocessed to ensure that they are all in the same format and size. 2. Loss Function: A loss function is used to measure how well the CNN is performing on the training data. The loss function is typically calculated by taking the difference between the predicted labels and the actual labels of the training images. 3. Optimizer: An optimizer is used to update the weights of the CNN in order to minimize the loss function. 4. Backpropagation: Backpropagation is a technique used to calculate the gradients of the loss function with respect to the weights of the CNN. The gradients are then used to update the weights of the CNN using the optimizer. ## How to Evaluate CNN Models Efficiency of CNN can be evaluated using a variety of criteria. Among the most popular metrics are: - Accuracy: Accuracy is the percentage of test images that the CNN correctly classifies. - Precision: Precision is the percentage of test images that the CNN predicts as a particular class and that are actually of that class. - Recall: Recall is the percentage of test images that are of a particular class and that the CNN predicts as that class. - F1 Score: The F1 Score is a harmonic mean of precision and recall. It is a good metric for evaluating the performance of a CNN on classes that are imbalanced. ## Different Types of CNN Models 1\. LeNet: [LeNet](https://www.geeksforgeeks.org/computer-vision/what-is-lenet/) developed by Yann LeCun and his colleagues in the late 1990s was one of the first successful CNNs designed for handwritten digit recognition. It laid the foundation for modern CNNs and achieved high accuracy on the MNIST dataset which contains 70,000 images of handwritten digits (0-9). 2\. AlexNet: [AlexNet](https://www.geeksforgeeks.org/machine-learning/ml-getting-started-with-alexnet/) is a CNN architecture that was developed by Alex Krizhevsky, Ilya Sutskever and Geoffrey Hinton in 2012. It was the first CNN to win the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) a major image recognition competition. It consists of several layers of convolutional and pooling layers followed by fully connected layers. The architecture includes five convolutional layers, three pooling layers and three fully connected layers. 3\. Resnet: [ResNets (Residual Networks)](https://www.geeksforgeeks.org/deep-learning/residual-networks-resnet-deep-learning/) are designed for image recognition and processing tasks. They are renowned for their ability to train very deep networks without overfitting making them highly effective for complex tasks. It introduces skip connections that allow the network to learn residual functions making it easier to train deep architecture. 4\. GoogleNet: [GoogleNet](https://www.geeksforgeeks.org/machine-learning/understanding-googlenet-model-cnn-architecture/) also known as InceptionNet is renowned for achieving high accuracy in image classification while using fewer parameters and computational resources compared to other state-of-the-art CNNs. The core component of GoogleNet allows the network to learn features at different scales simultaneously to enhance performance. 5\. VGG: [VGGs](https://www.geeksforgeeks.org/computer-vision/vgg-net-architecture-explained/) are developed by the Visual Geometry Group at Oxford, it uses small 3x3 convolutional filters stacked in multiple layers, creating a deep and uniform structure. Popular variants like VGG-16 and VGG-19 achieved state-of-the-art performance on the ImageNet dataset demonstrating the power of depth in CNNs. ## Applications - Image classification: CNNs are the state-of-the-art models for image classification. They can be used to classify images into different categories such as cats and dogs. - Object detection: It can be used to detect objects in images such as people, cars and buildings. They can also be used to localize objects in images which means that they can identify the location of an object in an image. - Image segmentation: It can be used to segment images which means that they can identify and label different objects in an image. This is useful for applications such as medical imaging and robotics. - Video analysis: It can be used to analyze videos such as tracking objects in a video or detecting events in a video. This is useful for applications such as video surveillance and traffic monitoring. ## Advantages - High Accuracy: They can achieve high accuracy in various image recognition tasks. - Efficiency: They are efficient, especially when implemented on GPUs. - Robustness: They are robust to noise and variations in input data. - Adaptability: It can be adapted to different tasks by modifying their architecture. ## Disadvantages - Complexity: It can be complex and difficult to train, especially for large datasets. - Resource-Intensive: It require significant computational resources for training and deployment. - Data Requirements: They need large amounts of labeled data for training. - Interpretability: They can be difficult to interpret making it challenging to understand their predictions. Convolutional Neural Network (CNN) in Deep Learning Suggested Quiz ![reset](https://media.geeksforgeeks.org/auth-dashboard-uploads/Reset-icon---Light.svg) 6 Questions What is the primary function of convolutional layers in a CNN? - A To reduce the size of the image - B To detect features like edges and textures - C To classify images - D To add non-linearity What is the role of activation functions in CNNs? - A To normalize data - B To introduce non-linearity - C To reduce computational complexity - D To generate labels Which CNN architecture first won the ImageNet competition in 2012? - A LeNet - B AlexNet - C VGG - D ResNet What is the key innovation introduced by ResNet architecture? - A Inception modules - B Skip connections - C Dropout layers - D Fully connected layers What type of problem is typically solved using CNNs in medical imaging? - A Disease detection - B Sentiment analysis - C Time series prediction - D Language translation Which CNN model is known for using small 3x3 convolution filters with deep layers? - A GoogleNet - B AlexNet - C VGG - D ResNet ![success](https://media.geeksforgeeks.org/auth-dashboard-uploads/sucess-img.png) Quiz Completed Successfully Your Score :0/6 Accuracy :0% Login to View Explanation 1**/6 \< Previous Next \> Comment [G](https://www.geeksforgeeks.org/user/goelaparna1520/) [goelaparna1520](https://www.geeksforgeeks.org/user/goelaparna1520/) 52 Article Tags: Article Tags: [Deep Learning](https://www.geeksforgeeks.org/category/ai-ml-ds/deep-learning/) [AI-ML-DS](https://www.geeksforgeeks.org/category/ai-ml-ds/) [Neural Network](https://www.geeksforgeeks.org/tag/neural-network/) ### Explore [![GeeksforGeeks](https://media.geeksforgeeks.org/auth-dashboard-uploads/gfgFooterLogo.png)](https://www.geeksforgeeks.org/) ![location](https://media.geeksforgeeks.org/img-practice/Location-1685004904.svg) Corporate & Communications Address: A-143, 7th Floor, Sovereign Corporate Tower, Sector- 136, Noida, Uttar Pradesh (201305) ![location](https://media.geeksforgeeks.org/img-practice/Location-1685004904.svg) Registered Address: K 061, Tower K, Gulshan Vivante Apartment, Sector 137, Noida, Gautam Buddh Nagar, Uttar Pradesh, 201305 [![GFG App on Play Store](https://media.geeksforgeeks.org/auth-dashboard-uploads/googleplay-%281%29.png)](https://geeksforgeeksapp.page.link/gfg-app)[![GFG App on App Store](https://media.geeksforgeeks.org/auth-dashboard-uploads/appstore-%281%29.png)](https://geeksforgeeksapp.page.link/gfg-app) - Company - [About Us](https://www.geeksforgeeks.org/about/) - [Legal](https://www.geeksforgeeks.org/legal/) - [Privacy Policy](https://www.geeksforgeeks.org/legal/privacy-policy/) - [Contact Us](https://www.geeksforgeeks.org/about/contact-us/) - [Advertise with us](https://www.geeksforgeeks.org/advertise-with-us/) - [GFG Corporate Solution](https://www.geeksforgeeks.org/gfg-corporate-solution/) - [Campus Training Program](https://www.geeksforgeeks.org/campus-training-program/) - Explore - [POTD](https://www.geeksforgeeks.org/problem-of-the-day) - [Job-A-Thon](https://practice.geeksforgeeks.org/events/rec/job-a-thon/) - [Blogs](https://www.geeksforgeeks.org/category/blogs/?type=recent) - [Nation Skill Up](https://www.geeksforgeeks.org/nation-skill-up/) - Tutorials - [Programming Languages](https://www.geeksforgeeks.org/computer-science-fundamentals/programming-language-tutorials/) - [DSA](https://www.geeksforgeeks.org/dsa/dsa-tutorial-learn-data-structures-and-algorithms/) - [Web Technology](https://www.geeksforgeeks.org/web-tech/web-technology/) - [AI, ML & Data Science](https://www.geeksforgeeks.org/machine-learning/ai-ml-and-data-science-tutorial-learn-ai-ml-and-data-science/) - [DevOps](https://www.geeksforgeeks.org/devops/devops-tutorial/) - [CS Core Subjects](https://www.geeksforgeeks.org/gate/gate-exam-tutorial/) - [Interview Preparation](https://www.geeksforgeeks.org/aptitude/interview-corner/) - [Software and Tools](https://www.geeksforgeeks.org/websites-apps/software-and-tools-a-to-z-list/) - Courses - [ML and Data Science](https://www.geeksforgeeks.org/courses/category/machine-learning-data-science) - [DSA and Placements](https://www.geeksforgeeks.org/courses/category/dsa-placements) - [Web Development](https://www.geeksforgeeks.org/courses/category/development-testing) - [Programming Languages](https://www.geeksforgeeks.org/courses/category/programming-languages) - [DevOps & Cloud](https://www.geeksforgeeks.org/courses/category/cloud-devops) - [GATE](https://www.geeksforgeeks.org/courses/category/gate) - [Trending Technologies](https://www.geeksforgeeks.org/courses/category/trending-technologies/) - Videos - [DSA](https://www.geeksforgeeks.org/videos/category/sde-sheet/) - [Python](https://www.geeksforgeeks.org/videos/category/python/) - [Java](https://www.geeksforgeeks.org/videos/category/java-w6y5f4/) - [C++](https://www.geeksforgeeks.org/videos/category/c/) - [Web Development](https://www.geeksforgeeks.org/videos/category/web-development/) - [Data Science](https://www.geeksforgeeks.org/videos/category/data-science/) - [CS Subjects](https://www.geeksforgeeks.org/videos/category/cs-subjects/) - Preparation Corner - [Interview Corner](https://www.geeksforgeeks.org/interview-prep/interview-corner/) - [Aptitude](https://www.geeksforgeeks.org/aptitude/aptitude-questions-and-answers/) - [Puzzles](https://www.geeksforgeeks.org/aptitude/puzzles/) - [GfG 160](https://www.geeksforgeeks.org/courses/gfg-160-series) - [System Design](https://www.geeksforgeeks.org/system-design/system-design-tutorial/) [@GeeksforGeeks, Sanchhaya Education Private Limited](https://www.geeksforgeeks.org/), [All rights reserved](https://www.geeksforgeeks.org/copyright-information/) ![]()
Readable Markdown	Convolutional Neural Network (CNN) in Deep Learning Suggested Quiz 6 Questions What is the primary function of convolutional layers in a CNN? - A To reduce the size of the image - B To detect features like edges and textures - C To classify images - D To add non-linearity What is the role of activation functions in CNNs? - A To normalize data - B To introduce non-linearity - C To reduce computational complexity - D To generate labels Which CNN architecture first won the ImageNet competition in 2012? - A LeNet - B AlexNet - C VGG - D ResNet What is the key innovation introduced by ResNet architecture? - A Inception modules - B Skip connections - C Dropout layers - D Fully connected layers What type of problem is typically solved using CNNs in medical imaging? - A Disease detection - B Sentiment analysis - C Time series prediction - D Language translation Which CNN model is known for using small 3x3 convolution filters with deep layers? - A GoogleNet - B AlexNet - C VGG - D ResNet ![success](https://media.geeksforgeeks.org/auth-dashboard-uploads/sucess-img.png) Quiz Completed Successfully Your Score :0/6 Accuracy :0% Login to View Explanation 1/6
Shard	103 (laksa)
Root Hash	12046344915360636903
Unparsed URL	org,geeksforgeeks!www,/deep-learning/convolutional-neural-network-cnn-in-machine-learning/ s443