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Tag: convolutional neural networks

  • Make Your Own Neural Network: An In-depth Visual Introduction For Beginners

    Make Your Own Neural Network: An In-depth Visual Introduction For Beginners


    Price: $10.99
    (as of Dec 24,2024 02:24:52 UTC – Details)


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    Customers say

    Customers find the book provides an in-depth introduction to neural networks for beginners. They find it informative and engaging for those interested in AI, coding, and programming. However, some readers feel the pacing is inconsistent and the material appears repetitive at times.

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    Have you ever wanted to understand how neural networks work and how you can create your own from scratch? In this post, we will provide an in-depth visual introduction for beginners on how to make your own neural network.

    Neural networks are a type of artificial intelligence that mimics the way the human brain works. They are made up of interconnected layers of nodes, or artificial neurons, that process and learn from data. By understanding the basics of neural networks, you can unlock their potential for solving complex problems and making intelligent decisions.

    To get started, we will guide you through the step-by-step process of creating a simple neural network using Python and the popular deep learning library, TensorFlow. We will cover the basics of how neural networks work, including layers, activation functions, and loss functions, and show you how to train your network on a dataset.

    Through visual aids and hands-on examples, you will gain a solid understanding of the fundamentals of neural networks and be able to apply this knowledge to build your own models. Whether you are a beginner in the field of artificial intelligence or looking to expand your skills, this post will provide you with a clear and accessible introduction to making your own neural network. Let’s dive in and start creating!
    #Neural #Network #Indepth #Visual #Introduction #Beginners

  • CONVOLUTIONAL NEURAL NETWORKS Examples with MATLAB

    CONVOLUTIONAL NEURAL NETWORKS Examples with MATLAB


    Price: $35.95
    (as of Dec 24,2024 01:38:15 UTC – Details)


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    Publisher ‏ : ‎ Scientific Books (August 2, 2023)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 247 pages
    ISBN-10 ‏ : ‎ 1446781534
    ISBN-13 ‏ : ‎ 978-1446781531
    Item Weight ‏ : ‎ 1.22 pounds
    Dimensions ‏ : ‎ 7 x 0.56 x 10 inches

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    Convolutional Neural Networks (CNNs) are a type of deep learning model commonly used for image recognition and classification tasks. In this post, we will explore some examples of Convolutional Neural Networks implemented in MATLAB.

    Example 1: Image Classification using CNN
    One common application of CNNs is image classification. In this example, we will use the CIFAR-10 dataset, which consists of 60,000 32×32 color images in 10 classes, to train a CNN model for image classification. We will use MATLAB’s Deep Learning Toolbox to create and train the CNN model.

    Example 2: Object Detection using CNN
    CNNs can also be used for object detection tasks, where the goal is to not only classify the objects in an image but also localize and identify their positions. In this example, we will use the COCO dataset, which contains images with multiple objects labeled with bounding boxes, to train a CNN model for object detection. We will use MATLAB’s Computer Vision Toolbox to implement the object detection algorithm.

    Example 3: Facial Recognition using CNN
    Facial recognition is another popular application of CNNs, where the goal is to identify and verify individuals based on their facial features. In this example, we will use a dataset of labeled face images to train a CNN model for facial recognition. We will use MATLAB’s Image Processing Toolbox to preprocess the images and create the CNN model.

    By implementing these examples in MATLAB, you can gain a better understanding of how Convolutional Neural Networks work and how they can be applied to various image processing tasks. The code for these examples can be easily adapted and customized for your own projects and datasets.
    #CONVOLUTIONAL #NEURAL #NETWORKS #Examples #MATLAB

  • Convolutional Neural Networks for Medical Applications (SpringerBriefs in Com…

    Convolutional Neural Networks for Medical Applications (SpringerBriefs in Com…



    Convolutional Neural Networks for Medical Applications (SpringerBriefs in Com…

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    Convolutional Neural Networks (CNNs) have revolutionized the field of medical imaging by providing state-of-the-art performance in various medical applications. In this post, we will explore the latest advancements in CNNs for medical applications, as discussed in the book “Convolutional Neural Networks for Medical Applications” from the SpringerBriefs in Computer Science series.

    CNNs have shown remarkable results in tasks such as image classification, object detection, segmentation, and image generation. In the medical field, CNNs have been used for diagnosing diseases, detecting abnormalities in medical images, and even predicting patient outcomes.

    The book covers a wide range of topics, including the fundamentals of CNNs, different architectures used in medical imaging, data preprocessing techniques, and the challenges faced in applying CNNs to medical data. It also discusses the ethical considerations and regulatory requirements that need to be addressed when using CNNs in healthcare.

    Overall, “Convolutional Neural Networks for Medical Applications” provides a comprehensive overview of the current state of CNNs in the medical field and offers valuable insights for researchers, practitioners, and students interested in leveraging CNNs for medical applications. If you are interested in the intersection of deep learning and healthcare, this book is a must-read.
    #Convolutional #Neural #Networks #Medical #Applications #SpringerBriefs #Com..

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