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Explain how autoencoder works and mention some of its applications Explain the following code

Explain how autoencoder works and mention some of its applications Explain the following code

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
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Autoencoder and VAE a) Explain how autoencoder works and mention some of its applications b) Explain the following code 1 input layers. Input (shape=(28, 28, 1)) 2 3 x = layers.Conv2D(32, (3, 3), activation='relu", padding="same") (input) 4 x = layers.MaxPooling20((2, 2), padding="same") (x) 5 x = layers.Conv2D(32, (3, 3), activation="relu", padding="same") (x) x = layers.MaxPooling20((2, 2), padding="same") (x) 7 8 x = layers.Conv2DTranspose (32, (3, 3), strides=2, activation='relu", padding="same") (x) 9 x = layers.Conv2DTranspose (32, (3, 3), strides=2, activation='relu', padding="same") (x) 10 I x = layers.Conv2D(1, (3, 3), activation="sigmoid", padding="same") (x) 11 12 autoencoder Model (input, x) 13 autoencodex.compile(optimizer="adan", loss="binary_crossentropy") 14 autoencoder.summary()
Transcribed Image Text:Autoencoder and VAE a) Explain how autoencoder works and mention some of its applications b) Explain the following code 1 input layers. Input (shape=(28, 28, 1)) 2 3 x = layers.Conv2D(32, (3, 3), activation='relu", padding="same") (input) 4 x = layers.MaxPooling20((2, 2), padding="same") (x) 5 x = layers.Conv2D(32, (3, 3), activation="relu", padding="same") (x) x = layers.MaxPooling20((2, 2), padding="same") (x) 7 8 x = layers.Conv2DTranspose (32, (3, 3), strides=2, activation='relu", padding="same") (x) 9 x = layers.Conv2DTranspose (32, (3, 3), strides=2, activation='relu', padding="same") (x) 10 I x = layers.Conv2D(1, (3, 3), activation="sigmoid", padding="same") (x) 11 12 autoencoder Model (input, x) 13 autoencodex.compile(optimizer="adan", loss="binary_crossentropy") 14 autoencoder.summary()
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