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1 Programming Assignment 1. Convolutional neural networks (CNNs) • Mission: Write Keras code to implement the CNN model. ● Data set: Fashion MNIST dataset Read the data set description. This is a dataset of 60,000 28 × 28 grayscale images of 10 fashion categories, along with a test set of 10, 000 images. • Approaches: CNN model (required): Don't use any built-in CNN architectures such VGG or ResNet. Please use keras library to build a simple CNN model: Keras. Other data pre-processing or feature engineering methods (optional): You can apply any technique you prefer. • Performance metric: Successfully train the CNN model using the training data and provide me the accuracy for the test data. Reference 1. Convolutional Neural Network (CNN)

1 Programming Assignment 1. Convolutional neural networks (CNNs) • Mission: Write Keras code to implement the CNN model. ● Data set: Fashion MNIST dataset Read the data set description. This is a dataset of 60,000 28 × 28 grayscale images of 10 fashion categories, along with a test set of 10, 000 images. • Approaches: CNN model (required): Don't use any built-in CNN architectures such VGG or ResNet. Please use keras library to build a simple CNN model: Keras. Other data pre-processing or feature engineering methods (optional): You can apply any technique you prefer. • Performance metric: Successfully train the CNN model using the training data and provide me the accuracy for the test data. Reference 1. Convolutional Neural Network (CNN)

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1 Programming Assignment 1. Convolutional neural networks (CNNs) Mission: Write Keras code to implement the CNN model. • Data set: Fashion MNIST dataset Read the data set description. This is a dataset of 60, 000 28 × 28 grayscale images of 10 fashion categories, along with a test set of 10, 000 images. - • Approaches: CNN model (required): Don't use any built-in CNN architectures such VGG or ResNet. Please use keras library to build a simple CNN model: Keras. Other data pre-processing or feature engineering methods (optional): You can apply any technique you prefer. • Performance metric: Successfully train the CNN model using the training data and provide me the accuracy for the test data. Reference 1. Convolutional Neural Network (CNN)
Transcribed Image Text:1 Programming Assignment 1. Convolutional neural networks (CNNs) Mission: Write Keras code to implement the CNN model. • Data set: Fashion MNIST dataset Read the data set description. This is a dataset of 60, 000 28 × 28 grayscale images of 10 fashion categories, along with a test set of 10, 000 images. - • Approaches: CNN model (required): Don't use any built-in CNN architectures such VGG or ResNet. Please use keras library to build a simple CNN model: Keras. Other data pre-processing or feature engineering methods (optional): You can apply any technique you prefer. • Performance metric: Successfully train the CNN model using the training data and provide me the accuracy for the test data. Reference 1. Convolutional Neural Network (CNN)
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