CONVOLUTIONAL NEURAL NETWORKS Examples with MATLAB

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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.
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