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Tag: Convolutional

  • Early Soft Error Reliability Assessment of Convolutional Neural Networks Executi

    Early Soft Error Reliability Assessment of Convolutional Neural Networks Executi



    Early Soft Error Reliability Assessment of Convolutional Neural Networks Executi

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    ve on FPGA Platforms

    Soft errors, also known as transient faults, pose a significant threat to the reliability of hardware systems, including Field Programmable Gate Arrays (FPGAs) used for executing Convolutional Neural Networks (CNNs). These errors can occur due to various factors such as cosmic radiation, electrical noise, and manufacturing defects, leading to incorrect computations and potentially compromising the accuracy of the neural network.

    In order to ensure the reliability of CNN execution on FPGA platforms, it is crucial to perform early assessment of soft error vulnerability. This involves evaluating the susceptibility of the hardware to soft errors and implementing mitigation strategies to minimize their impact on the network’s performance.

    One approach to assessing soft error reliability is to use fault injection techniques to simulate the effects of soft errors on the FPGA. By introducing faults into the system and monitoring the network’s behavior, researchers can identify potential weak points and develop strategies to improve fault tolerance.

    In addition to fault injection, researchers can also leverage tools such as fault-tolerant design techniques and error detection and correction algorithms to enhance the reliability of CNN execution on FPGA platforms.

    Overall, early soft error reliability assessment is essential for ensuring the robustness of CNNs executed on FPGAs, particularly in safety-critical applications where accuracy and reliability are paramount. By proactively addressing soft error vulnerabilities, researchers can improve the overall reliability and performance of neural networks on FPGA platforms.
    #Early #Soft #Error #Reliability #Assessment #Convolutional #Neural #Networks #Executi

  • The Architecture of Convnets and Data Processing. Advantages of Convolutional Neural Networks

    The Architecture of Convnets and Data Processing. Advantages of Convolutional Neural Networks


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    (as of Dec 26,2024 14:32:47 UTC – Details)


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    Publisher ‏ : ‎ Grin Verlag (September 7, 2020)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 32 pages
    ISBN-10 ‏ : ‎ 3346213080
    ISBN-13 ‏ : ‎ 978-3346213082
    Item Weight ‏ : ‎ 1.9 ounces
    Dimensions ‏ : ‎ 5.83 x 0.08 x 8.27 inches

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    Convolutional Neural Networks (CNNs) have revolutionized the field of computer vision with their ability to effectively process and analyze visual data. The architecture of CNNs plays a crucial role in their success, allowing them to efficiently extract features from images and make accurate predictions. In this post, we will explore the architecture of CNNs and their advantages in data processing.

    CNNs are composed of multiple layers, each with a specific function in processing visual data. The input layer receives the raw pixel values of an image, which are then passed through a series of convolutional layers. These layers apply filters to the input image, extracting features such as edges, textures, and shapes. The output of each convolutional layer is passed through an activation function, such as ReLU, to introduce non-linearity into the network.

    Pooling layers are then used to downsample the output of the convolutional layers, reducing the spatial dimensions of the feature maps while retaining important information. This helps in reducing the computational complexity of the network and preventing overfitting. Finally, fully connected layers are used to classify the features extracted by the convolutional layers and make predictions.

    One of the key advantages of CNNs is their ability to learn hierarchical features from images. By using multiple layers of convolution and pooling, CNNs can automatically learn and extract complex patterns and structures from images, without the need for manual feature extraction. This makes CNNs highly effective in tasks such as image classification, object detection, and image segmentation.

    Furthermore, CNNs are also able to handle spatial invariance, meaning they can recognize objects in different positions and orientations within an image. This is achieved through the use of shared weights in the convolutional layers, allowing the network to learn features that are invariant to translation and rotation.

    In conclusion, the architecture of Convolutional Neural Networks plays a crucial role in their success in processing visual data. By efficiently extracting features from images and learning hierarchical representations, CNNs have become a powerful tool in computer vision tasks. Their ability to handle spatial invariance and automatically learn features make them highly effective in tasks such as image classification, object detection, and image segmentation.
    #Architecture #Convnets #Data #Processing #Advantages #Convolutional #Neural #Networks

  • A Guide to Convolutional Neural Networks for Computer Vision by Salman Khan (Eng

    A Guide to Convolutional Neural Networks for Computer Vision by Salman Khan (Eng



    A Guide to Convolutional Neural Networks for Computer Vision by Salman Khan (Eng

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    Convolutional Neural Networks (CNNs) have revolutionized the field of computer vision, allowing machines to understand and interpret visual data with remarkable accuracy. In this guide, we will delve into the inner workings of CNNs and explore how they are used in computer vision applications.

    First, let’s start with the basics. A CNN is a type of deep neural network that is specifically designed to process visual data. It is inspired by the organization of the visual cortex in the human brain, where neurons are arranged in layers and respond to specific visual stimuli.

    One of the key components of a CNN is the convolutional layer. This layer applies a series of filters to the input image, extracting features such as edges, textures, and shapes. These features are then passed on to subsequent layers for further processing.

    Another important component of a CNN is the pooling layer, which reduces the spatial dimensions of the feature maps while retaining important information. This helps to make the network more robust to variations in the input data.

    CNNs are typically trained using a large dataset of labeled images, where the network learns to recognize patterns and objects in the images. The network is trained using an optimization algorithm such as gradient descent, which adjusts the weights of the network to minimize the prediction error.

    Once the CNN is trained, it can be used to make predictions on new, unseen images. The network processes the input image through its layers, extracting features and making a prediction based on the learned patterns.

    CNNs have been used in a wide range of computer vision applications, including image classification, object detection, and image segmentation. They have achieved state-of-the-art performance on tasks such as image recognition and visual question answering.

    In conclusion, Convolutional Neural Networks are a powerful tool for computer vision tasks, allowing machines to understand and interpret visual data with remarkable accuracy. By understanding the inner workings of CNNs and how they are used in practice, you can harness the power of these networks for your own computer vision projects.
    #Guide #Convolutional #Neural #Networks #Computer #Vision #Salman #Khan #Eng

  • Convolutional Neural Network in classifying scanned documents

    Convolutional Neural Network in classifying scanned documents


    Price: $42.90 – $3.85
    (as of Dec 26,2024 13:53:33 UTC – Details)


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    Publisher ‏ : ‎ Grin Verlag (January 5, 2017)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 40 pages
    ISBN-10 ‏ : ‎ 3668371687
    ISBN-13 ‏ : ‎ 978-3668371682
    Item Weight ‏ : ‎ 2.34 ounces
    Dimensions ‏ : ‎ 5.83 x 0.1 x 8.27 inches

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    Convolutional Neural Networks (CNNs) have revolutionized the field of image recognition and classification, but did you know they can also be highly effective in classifying scanned documents?

    Scanned documents often pose a challenge for traditional machine learning algorithms due to variations in image quality, orientation, and text size. However, CNNs are adept at learning hierarchical features from raw pixel data, making them well-suited for the task of document classification.

    By leveraging the spatial relationships between pixels, CNNs can automatically extract relevant features from scanned documents, such as text, logos, signatures, and other visual elements. This allows them to accurately categorize documents into different classes, such as invoices, receipts, contracts, and more.

    Furthermore, CNNs can be trained on large datasets of labeled scanned documents to learn complex patterns and variations in document layouts. This enables them to generalize well to unseen documents and achieve high levels of accuracy in classification tasks.

    In summary, CNNs offer a powerful and efficient solution for classifying scanned documents, making them an invaluable tool for document processing and information retrieval tasks. If you’re interested in learning more about how CNNs can be applied to document classification, stay tuned for upcoming posts on this topic!
    #Convolutional #Neural #Network #classifying #scanned #documents

  • Guide to Convolutional Neural Networks: A Pr… by Jahani Heravi, Elnaz Hardback

    Guide to Convolutional Neural Networks: A Pr… by Jahani Heravi, Elnaz Hardback



    Guide to Convolutional Neural Networks: A Pr… by Jahani Heravi, Elnaz Hardback

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    Guide to Convolutional Neural Networks: A Practical Overview by Jahani Heravi and Elnaz Hardback

    In this comprehensive guide, authors Jahani Heravi and Elnaz Hardback provide a practical overview of Convolutional Neural Networks (CNNs) – a powerful deep learning technique widely used in image recognition, computer vision, and natural language processing.

    This book is designed for beginners and intermediate learners interested in understanding CNNs and how they can be applied in various real-world applications. The authors cover the fundamental concepts of CNNs, including convolutional layers, pooling layers, activation functions, and fully connected layers.

    Readers will also learn about key techniques for improving the performance of CNNs, such as data augmentation, transfer learning, and hyperparameter tuning. The authors provide hands-on examples and code snippets using popular deep learning frameworks like TensorFlow and PyTorch.

    Whether you are a student, researcher, or industry professional looking to enhance your knowledge of CNNs, this guide is a valuable resource to help you master this essential deep learning technique.
    #Guide #Convolutional #Neural #Networks #Pr.. #Jahani #Heravi #Elnaz #Hardback

  • Face Image Analysis with Convolutional Neural Networks

    Face Image Analysis with Convolutional Neural Networks


    Price: $144.90 – $65.11
    (as of Dec 26,2024 13:10:28 UTC – Details)


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    Publisher ‏ : ‎ Grin Publishing (August 13, 2009)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 198 pages
    ISBN-10 ‏ : ‎ 3640397169
    ISBN-13 ‏ : ‎ 978-3640397167
    Item Weight ‏ : ‎ 1.08 pounds
    Dimensions ‏ : ‎ 8.27 x 0.42 x 11.69 inches

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    Face Image Analysis with Convolutional Neural Networks

    Convolutional Neural Networks (CNNs) have revolutionized the field of image analysis, particularly when it comes to tasks like facial recognition. By leveraging the power of deep learning, CNNs are able to automatically learn features from raw image data, making them incredibly effective at tasks like identifying individuals in photos.

    In the context of face image analysis, CNNs can be used for a variety of tasks, such as facial expression recognition, gender classification, age estimation, and even detecting emotions. These networks are able to extract high-level features from facial images, such as the shape of the eyes, the position of the mouth, and the overall structure of the face.

    One of the key advantages of using CNNs for face image analysis is their ability to generalize well to unseen data. By training the network on a large dataset of labeled facial images, the CNN is able to learn patterns that can be applied to new, unseen images. This makes CNNs highly effective at tasks like facial recognition, where the goal is to identify individuals across different images.

    Overall, CNNs have proven to be a powerful tool for face image analysis, offering state-of-the-art performance on a wide range of tasks. As the field of deep learning continues to advance, we can expect even more impressive results from CNNs in the realm of facial analysis.
    #Face #Image #Analysis #Convolutional #Neural #Networks

  • Convolutional Neural Networks for Medical Applications, Paperback by Teoh, Te…

    Convolutional Neural Networks for Medical Applications, Paperback by Teoh, Te…



    Convolutional Neural Networks for Medical Applications, Paperback by Teoh, Te…

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    Convolutional Neural Networks for Medical Applications: A Comprehensive Guide

    If you’re interested in learning about the intersection of artificial intelligence and healthcare, look no further than this comprehensive guide on Convolutional Neural Networks (CNNs) for medical applications. Authored by leading experts in the field, Teoh and Te bring their expertise to this groundbreaking book that explores the potential of CNNs in revolutionizing medical diagnosis and treatment.

    In this paperback, readers will discover the fundamentals of CNNs and how they can be applied to various medical tasks such as image analysis, disease detection, and personalized medicine. With real-world case studies and practical examples, this book provides a hands-on approach to understanding CNNs and their potential in the healthcare industry.

    Whether you’re a healthcare professional looking to enhance your knowledge of AI or a tech enthusiast interested in the future of medicine, this book is a must-read. Stay ahead of the curve and dive into the world of CNNs for medical applications with Teoh and Te’s insightful guide.
    #Convolutional #Neural #Networks #Medical #Applications #Paperback #Teoh #Te..

  • Reconhecimento das Emoções Faciais usando Convolutional Neural Network (Portuguese Edition)

    Reconhecimento das Emoções Faciais usando Convolutional Neural Network (Portuguese Edition)


    Price: $48.00
    (as of Dec 26,2024 12:32:06 UTC – Details)


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    Publisher ‏ : ‎ Edições Nosso Conhecimento (April 11, 2023)
    Language ‏ : ‎ Portuguese
    Paperback ‏ : ‎ 64 pages
    ISBN-10 ‏ : ‎ 6205887630
    ISBN-13 ‏ : ‎ 978-6205887639
    Item Weight ‏ : ‎ 3.67 ounces
    Dimensions ‏ : ‎ 5.91 x 0.15 x 8.66 inches

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    Reconhecimento das Emoções Faciais usando Convolutional Neural Network

    O reconhecimento das emoções faciais é uma área de pesquisa em constante evolução, com aplicações em diversas áreas, como segurança, saúde mental e interação humano-máquina. Uma das abordagens mais eficazes para realizar essa tarefa é o uso de redes neurais convolucionais (CNNs).

    As CNNs são um tipo de rede neural especialmente adequado para processar imagens, devido à sua capacidade de capturar padrões espaciais e hierárquicos. Ao treinar uma CNN em um conjunto de dados de imagens rotuladas com emoções faciais, é possível extrair automaticamente características relevantes e aprender a classificar corretamente as emoções representadas nas imagens.

    Este post irá explorar o uso de CNNs no reconhecimento de emoções faciais, discutindo a arquitetura de uma CNN típica para esse fim, o processo de treinamento e avaliação do modelo, bem como as aplicações práticas dessa tecnologia. Além disso, serão abordadas algumas das principais limitações e desafios enfrentados ao utilizar CNNs para reconhecimento de emoções faciais, e possíveis direções futuras de pesquisa nessa área.

    Fique ligado para mais informações e novidades sobre reconhecimento de emoções faciais usando Convolutional Neural Networks!
    #Reconhecimento #das #Emoções #Faciais #usando #Convolutional #Neural #Network #Portuguese #Edition

  • Advanced Applied Deep Learning : Convolutional Neural Networks and Object Det…

    Advanced Applied Deep Learning : Convolutional Neural Networks and Object Det…



    Advanced Applied Deep Learning : Convolutional Neural Networks and Object Det…

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    In this post, we will delve into the world of advanced applied deep learning, focusing specifically on Convolutional Neural Networks (CNNs) and Object Detection. CNNs have revolutionized the field of computer vision by enabling machines to learn hierarchical representations of visual data, leading to significant advancements in tasks such as image classification, object detection, and segmentation.

    Object detection is a crucial task in computer vision, where the goal is to identify and localize objects within an image. CNNs have proven to be highly effective in this domain, as they can automatically learn features that are relevant for distinguishing between different objects. By utilizing techniques such as region-based CNNs, single-shot detectors, and feature pyramid networks, researchers have developed state-of-the-art object detection models that are capable of achieving high levels of accuracy and speed.

    One of the most popular object detection frameworks is the Region-based Convolutional Neural Network (R-CNN) family, which includes models like Faster R-CNN, Mask R-CNN, and Cascade R-CNN. These models have been widely used in various applications, including autonomous driving, surveillance, and medical imaging. They are able to not only detect objects within an image but also provide precise bounding box coordinates and segmentation masks.

    In addition to object detection, CNNs are also being applied to other challenging tasks such as instance segmentation, where the goal is to identify individual instances of objects within an image and segment them accordingly. Models like Mask R-CNN have been at the forefront of instance segmentation, achieving impressive results on benchmark datasets like COCO.

    Overall, CNNs have revolutionized the field of computer vision and continue to push the boundaries of what is possible in terms of object detection and related tasks. As researchers continue to develop more advanced architectures and training techniques, we can expect further advancements in this exciting field. Stay tuned for more updates on the latest developments in advanced applied deep learning!
    #Advanced #Applied #Deep #Learning #Convolutional #Neural #Networks #Object #Det..

  • Tree-Based Convolutional Neural Networks – 9789811318696

    Tree-Based Convolutional Neural Networks – 9789811318696



    Tree-Based Convolutional Neural Networks – 9789811318696

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    Tree-Based Convolutional Neural Networks – 9789811318696

    In the world of deep learning and artificial intelligence, convolutional neural networks (CNNs) have been the go-to model for image recognition tasks. However, a new approach called Tree-Based Convolutional Neural Networks is making waves in the field.

    This innovative technique combines the power of traditional CNNs with the hierarchical structure of tree-based models, resulting in improved performance and accuracy. In the book “Tree-Based Convolutional Neural Networks,” readers will learn about the theory behind this approach, as well as practical applications and case studies.

    With the ISBN number 9789811318696, this book is a must-read for anyone interested in pushing the boundaries of deep learning and exploring new avenues for image recognition. Dive into the world of Tree-Based Convolutional Neural Networks and discover the future of AI.
    #TreeBased #Convolutional #Neural #Networks

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