Applications of AI for Anomaly Detection

Applications of AI for Anomaly DetectionAAADNVNvidiaNV-AAAD1.0<ul> <li>Prepare data and build, train, and evaluate models using XGBoost, autoencoders, and GANs</li><li>Detect anomalies in datasets with both labeled and unlabeled data</li><li>Classify anomalies into multiple categories regardless of whether the original data was labeled</li></ul><ul> <li>Professional data science experience using Python</li><li>Experience training deep neural networks</li></ul>Introduction <ul> <li>Meet the instructor.</li><li>Create an account at courses.nvidia.com/join</li></ul>Anomaly Detection in Network Data Using GPU-Accelerated XGBoost <ul> <li>Learn how to detect anomalies using supervised learning:<ul> <li>Prepare data for GPU acceleration using the provided dataset.</li><li>Train a binary and multi-class classifier using the popular machine learning algorithm XGBoost.</li><li>Assess and improve your model’s performance before deployment.</li></ul></li></ul>Anomaly Detection in Network Data Using GPU-Accelerated Autoencoder <ul> <li>Learn how to detect anomalies using modern unsupervised learning:<ul> <li>Build and train a deep learning-based autoencoder to work with unlabeled data.</li><li>Apply techniques to separate anomalies into multiple classes.</li><li>Explore other applications of GPU-accelerated autoencoders.</li></ul></li></ul>Project: Anomaly Detection in Network Data Using GANs <ul> <li>Learn how to detect anomalies using GANs:<ul> <li>Train an unsupervised learning model to create new data.</li><li>Use that new data to turn the problem into a supervised learning problem.</li><li>Compare the performance of this new approach to more established approaches.</li></ul></li></ul>Assessment and Q&A- Prepare data and build, train, and evaluate models using XGBoost, autoencoders, and GANs - Detect anomalies in datasets with both labeled and unlabeled data - Classify anomalies into multiple categories regardless of whether the original data was labeled- Professional data science experience using Python - Experience training deep neural networksIntroduction - Meet the instructor. - Create an account at courses.nvidia.com/join Anomaly Detection in Network Data Using GPU-Accelerated XGBoost - Learn how to detect anomalies using supervised learning: - Prepare data for GPU acceleration using the provided dataset. - Train a binary and multi-class classifier using the popular machine learning algorithm XGBoost. - Assess and improve your model’s performance before deployment. Anomaly Detection in Network Data Using GPU-Accelerated Autoencoder - Learn how to detect anomalies using modern unsupervised learning: - Build and train a deep learning-based autoencoder to work with unlabeled data. - Apply techniques to separate anomalies into multiple classes. - Explore other applications of GPU-accelerated autoencoders. Project: Anomaly Detection in Network Data Using GANs - Learn how to detect anomalies using GANs: - Train an unsupervised learning model to create new data. - Use that new data to turn the problem into a supervised learning problem. - Compare the performance of this new approach to more established approaches. Assessment and Q&A1 jour500.00500.00500.00500.00500.00420.00500.00690.00