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Simple Autoencoder](./1_Simple_Autoencoder/simple-autoencoder.ipynb)\n\nThe simples possible Autoencoder in which the encoder and decoder consists of a single fully-connected layer each.\n\n![Simple Autoencoder Result](./1_Simple_Autoencoder/simple_autoencoder.png)\n\n### [2. Deep Autoencoder](./2_Deep_Autoencoder/deep-autoencoder.ipynb)\n\nA Deep Autoencoder in which both the encoder and decoder consists of multiple fully-connected layer each.\n\n![Deep Autoencoder Result](./2_Deep_Autoencoder/deep_autoencoder.png)\n\n### [3. Deep Convolutional Autoencoder](./3_Deep_Convolutional_Autoencoder/deep-convolutional-autoencoder.ipynb)\n\nA Deep Autoencoder in which both the encoder and decoder consists of multiple fully-convolutional layer each.\n\n![Deep Convolutional Autoencoder Result](./3_Deep_Convolutional_Autoencoder/conv_autoencoder.png)\n\n### [4. Denoising Autoencoder](./4_Denoising_Autoencoder/denoising-documents-attempt-1.ipynb)\n\nA fully convolutional Autoencoder for Denoising images with noisy backgrounds.\n\n![Noisy Image](./4_Denoising_Autoencoder/noisy.png)\n\n![Denoised Image](./4_Denoising_Autoencoder/denoised.png)","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsoumik12345%2Fautoencoders","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsoumik12345%2Fautoencoders","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsoumik12345%2Fautoencoders/lists"}