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Image ClassificationDownload the CIFAR-10 dataset (see https://www.cs.toronto.edu/ kriz/cifar.html), and read the raw image data into a numpy array, or download the CIFAR-10 dataset using the scikit-learn library.Randomly select 100 images from each of the 10 different classes, and save the selected result as a new numpy array.Randomly partition the data into a training set (60%) and a testing set (40%).Apply the training set to train different CNN models (for example, modify the number of layer
Image ClassificationDownload the CIFAR-10 dataset (see https://ww…
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numerade.com
Project Highlights:🧩 Dataset: CIFAR-10 (32x32 color images, 10 classes)⚙️ Model: Sequential CNN built using TensorFlow & Keras🧠 Architecture:Conv2D & MaxPooling2D layersFlatten Dense… | Sana Ullah
Project Highlights:🧩 Dataset: CIFAR-10 (32x32 color images, 10 classe…
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