Anomaly Detection:

• Autoencoder for Anomaly Detection: Training an autoencoder to detect anomalies in data. The autoencoder is trained to reconstruct normal data, and it will struggle to reconstruct anomalies, leading to higher reconstruction error for anomalies.

pythonCopy codefrom tensorflow.keras import layers, models

# Build autoencoder model
input_dim = 28 * 28
encoding_dim = 64

# Encoder
input_img = layers.Input(shape=(input_dim,))
encoded = layers.Dense(encoding_dim, activation='relu')(input_img)

# Decoder
decoded = layers.Dense(input_dim, activation='sigmoid')(encoded)

# Autoencoder model
autoencoder = models.Model(input_img, decoded)

# Compile model
autoencoder.compile(optimizer='adam', loss='mean_squared_error')

# Train on normal data
autoencoder.fit(normal_data, normal_data, epochs=50, batch_size=256, shuffle=True)