An_Overview_of_Machine_Learning[1].pptx
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This document provides an overview of machine learning, including its history and major methods. It discusses Alan Turing's work on determining if machines can think and pass the Turing Test. Examples are given of projects like Eugene the chatbot and Google Duplex that have passed versions of the test. Supervised, unsupervised, and reinforcement learning techniques are defined at a high level. Applications of machine learning like IBM's Deep Blue chess program and Google's AlphaGo are mentioned. The document concludes that machine learning will continue to grow in importance and be applied to more areas.
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Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
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Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
20240609 QFM020 Irresponsible AI Reading List May 2024
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Essentials of Automations: The Art of Triggers and Actions in FME
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
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An_Overview_of_Machine_Learning[1].pptx
- 1. An Overview of Machine Learning Oswin Patrick Rozario 19ESKCS165 2022/09/25
- 3. What is machine learning? History of Machine Learning ML Methods Supervised Learning Unsupervised Learning Reinforcement Learning Advantages of ML Disadvantages of ML Use-Cases of ML Applications of ML Some Famous Personalities Conclusion Outline & Content
- 4. What is machine learning?
- 7. History Of Machine Learning
- 8. Alan Turing wanted to answer the question, CAN MACHINES THINK ? What is Turing test?
- 10. A chatbot named Eugene Goostman is accepted by some as the first to pass the Turing Test, in 2014. In 2018, Google Duplex was introduced at the annual Google I/O Developer Conference. The machine scheduled a hair salon appointment and interacted with a hair salon assistant via the phone as part of the conversation. Fun Facts About Turing Test!
- 11. Deep Blue was a supercomputer developed by IBM specifically for playing chess and was best known for being the first artificial intelligence construct to ever win a chess match against a reigning world champion, Grandmaster Garry Kasparov, under regular time controls. In its final configuration, the computer used 256 processors, with an ability to evaluate 200 million chess positions per second. IBM’s Deep Blue
- 14. ML METHODS
- 18. Unsupervised ML-
- 19. Unsupervised ML-
- 25. ML APPLICATIONS-
- 26. 1. Andrej Karpathy Some Famous Personalities
- 28. We have a simple overview of some techniques and algorithms in machine learning. Furthermore, there are more and more techniques apply machine learning as a solution. In the future, machine learning will play an important role in our daily life. Conclusion