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Applications of AI for Anomaly Detection

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Learn to detect anomalies in large data sets to identify network intrusions using supervised and unsupervised machine learning techniques, such as accelerated XGBoost, autoencoders, and generative adversarial networks (GANs).

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Applications of AI for Anomaly Detection (2 hrs)

Learn to detect anomalies in large data sets to identify network intrusions using supervised and unsupervised machine learning techniques, such as accelerated XGBoost, autoencoders, and generative adversarial networks (GANs).
PREREQUISITES:Experience with CNNs and Python
TOOLS AND FRAMEWORKS: Keras, GANs
LANGUAGES:English
DURATION:2 hours


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