![]() Since these images are noisy, they need a noise removal filter to classify and read the digits properly. MNIST is a manageable, beginner-friendly data source that can be used to generate images of handwritten numbers. You can create a handwriting recognition tool using the MNIST dataset as input. Now, let us discuss the applications of these networks. And to train the autoencoders, you can follow the same procedure as artificial neural networks via back-propagation. Binary cross-entropy and mean squared error are the two top choices for the loss function. To begin the development process, you will need an encoding method, a decoding method, and a loss function.
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