Welcome to Knowledge City's course on neural networks, practical applications. In these lessons you'll learn about anomaly and threat detection, automation, cybersecurity and predictive systems. You'll learn how you can use neural networks and anomaly and threat detection systems. Anomaly detection is a data mining process that identifies points, segments, patterns, or events in data that is that abnormal or unexpected. There are several traditional algorithms and methods for performing anomaly detection. However, there are also several new neural network based methods that have been developed to overcome the challenges and weaknesses of the traditional approaches. Threat detection is the practice of analyzing a security system, in order to identify any malicious activity that could compromise the network. Now I'm gonna go into a practical use case. One of the most sensitive systems and industries that nearly all people use is one of finance and banking. Because of the sensitive nature and the trust that the individuals must have for these systems, it is not only important that any threats or unauthorized access is detected, but also that any attempt is identified and prevented. This is one of the main motivations for intrusion detection and intrusion prevention systems, or IDS and IPS. Both in intrusion and attempted intrusion are serious threats to the banking system. And can be prevented through the incorporation of neural network based threat detection systems. Furthermore, when talking about IDS, the two main methods of detection are signature based and anomaly based detection. Both of which use neural networks. This concludes this lesson. And next up, I'm gonna talk about automation. Thank you.