816 Teaching courses in Rochford delivered Live Online

Duration 3 Days 18 CPD hours This course is intended for Participants are any IT specialist eager to become an industry recognized expert on Veeam software solutions:System Engineer / Administrator Backup / Virtualization Administrators Pre-Sales / Post-Sales / Solution Architects / Consultants Overview This certification is an excellent investment for an IT professional looking to increase productivity, reduce operating costs, increase potential for personal career advancement and gain recognition from the employer and customers. By completing the Veeam Certified Engineer (VMCE) course you should be able to:Get more functionality out of Veeam solutionsIdentify and resolve issues more efficientlyGain a distinction from your peers The Veeam Certified Engineer (VMCE) course is a 3 day technical deep-dive focused on teaching consultants and engineers to architect, implement, optimize and troubleshoot the Veeam software solution. Introduction Introduction Overview of Veeam products Hypervisor Support Veeam Backup & Replication key features Veeam One key features Veeam Management Pack vCloud Director Support Key concepts Backup Methods Scheduling Changed Block Tracking (CBT) Compression and Deduplication Retention Policy Veeam ONE features and functionality Auto Discovery of Backup and Virtual Infrastructure Business Categorization Pre-Defined Alerting Unattended monitoring Reporting and dashboards Agentless data gathering Hyper-V specific features Veeam One Deployment Veeam One assessment Tool Deployment Solution Architecture Deployment Scenarios Prerequisites Upgrading Veeam Backup & Replication Initial Configuration Adding Servers Adding a VMware Backup Proxy Adding a Hyper-V Offhost Backup Proxy Adding Backup Repositories Performing Configuration Backup and Restore Getting to Know User Interface Performing Backup Creating Backup Jobs Creating VM Copy Jobs Instant VM Recovery Replication (Replication Job, Failover, Failback) Insight into Replication Insight into Failover Insight into Failback SureBackup, SureReplica SureBackup Recovery Verification SureReplica Restore your VMs and Objects Data Recovery Restoring Microsoft Exchange and SharePoint objects Working with Veeam Backup & Replication Utilities 3-2-1 rule (Backup to tape, WAN, Backup Copy) Wan Accelerator Offsite Backup Copy Job Delegate file and VM restores with Veeam Backup Enterprise Manager Veeam Backup Enterprise Manager RESTful API HP StoreVirtual VSA Product Editions Product Editions Comparison Full and Free Troubleshooting How to review and analyze the issue Search for additional information Veeam Support Additional course details: Nexus Humans Veeam Certified Engineer (VMCE) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Veeam Certified Engineer (VMCE) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

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Duration 1 Days 6 CPD hours This course is intended for Learners who will find this course applicable to their work include: Solutions architects Cloud practitioners Data engineers Data scientists Developers Overview In this course, you will explore: Workload definition and key concepts The AWS Well-Architected Framework Review phases, process, best practices, and antipatterns High and medium risks Prioritizing improvements to the AWS Well-Architected workflow Locating and using the AWS Well-Architected Framework white paper, labs, prebuilt solutions in the AWS solutions library, AWS Well-Architected independent software vendors (ISVs), and AWS Well-Architected Partner Program (WAPP) This interactive course provides a deep dive into Amazon Web Services (AWS) best practices to help you perform effective and efficient AWS Well-Architected Framework Reviews. The course covers the phases of a review, including how to prepare, run, and get guidance after a review has been performed. Attendees should have familiarity with the AWS concepts, terminology, services, and tools that are covered in the intermediate, 200-levelAWS Well-Architected Best Practices.This course provides an AWS Well-Architected Framework Review simulation and instructor-led group exercises and discussions regarding prioritizing and solutioning risks. The content focuses on teaching learners how to prepare proposals on high and medium risk issues using the AWS Well-Architected Tool. Module 1: AWS Well-Architected Framework Reviews Workload definition Key concepts of a workload AWS Well-Architected Review phases AWS Well-Architected Review approach, lessons learned, and use case AWS Well-Architected Review best practices AWS Well-Architected Review anti-patterns Module 2: Customer Scenario Group Sessions Demonstration of a Review question and answer example Operational excellence Group role-play exercise Two questions in this pillar Security Group role-play exercise Three questions in this pillar Reliability Group role-play exercise Three questions in this pillar Performance efficiency Group role-play exercise Three questions in this pillar Cost optimization Group role-play exercise Three questions in this pillar Module 3: Risk Solutions and Priorities AWS Well-Architected workflow Defining and solutioning high risk issues (HRIs) and medium risk issues (MRIs) Identifying significant risks and solutioning group discussion for: Operational excellence Security Reliability Performance efficiency Cost optimization Prioritizing improvements Module 4: Resources Resource pages AWS Well-Architected ISVs Module 5: Course Summary Objective recap Debrief What?s next? Additional course details: Nexus Humans Advanced AWS Well-Architected Best Practices training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Advanced AWS Well-Architected Best Practices course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

El siguiente curso es una especialidad en el libro de Apocalipsis de la Biblia. Se trata sobre informar a los siervos de Dios de lo que va a suceder pronto para que estén preparados para la venida de Cristo.

Teaching4you is a tuition company that works to encourage and build confidence in students nationwide.

Teaching4you is a tuition company that works to encourage and build confidence in students nationwide.

Canelo Publishing Masterclasses. Book your space to peek behind the scenes of a trade publisher. Learn about the different roles and departments, how books are made and published, and how publishers interact with readers and booksellers. Hear what it takes to ensure your book gets published and becomes a hit. Learn about the key things a publisher looks for when they consider submissions or publish books. Canelo will cover questions commonly asked, as well as answering your questions live. The winners of the I Am In Print Novel Award 2023 will also be announced!

Duration 3 Days 18 CPD hours This course is intended for This course is geared for attendees with Intermediate IT skills who wish to learn Computer Vision with tensor flow 2 Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Working in a hands-on learning environment, led by our Computer Vision expert instructor, students will learn about and explore how to Build, train, and serve your own deep neural networks with TensorFlow 2 and Keras Apply modern solutions to a wide range of applications such as object detection and video analysis Run your models on mobile devices and web pages and improve their performance. Create your own neural networks from scratch Classify images with modern architectures including Inception and ResNet Detect and segment objects in images with YOLO, Mask R-CNN, and U-Net Tackle problems faced when developing self-driving cars and facial emotion recognition systems Boost your application's performance with transfer learning, GANs, and domain adaptation Use recurrent neural networks (RNNs) for video analysis Optimize and deploy your networks on mobile devices and in the browser Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. Hands-On Computervision with TensorFlow 2 is a hands-on course that thoroughly explores TensorFlow 2, the brandnew version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks. This course begins with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface. You'll then move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the course demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U-Net. You will also build generative dversarial networks (GANs) and variational autoencoders (VAEs) to create and edit images, and long short-term memory networks (LSTMs) to analyze videos. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts Computer Vision and Neural Networks Computer Vision and Neural Networks Technical requirements Computer vision in the wild A brief history of computer vision Getting started with neural networks TensorFlow Basics and Training a Model TensorFlow Basics and Training a Model Technical requirements Getting started with TensorFlow 2 and Keras TensorFlow 2 and Keras in detail The TensorFlow ecosystem Modern Neural Networks Modern Neural Networks Technical requirements Discovering convolutional neural networks Refining the training process Influential Classification Tools Influential Classification Tools Technical requirements Understanding advanced CNN architectures Leveraging transfer learning Object Detection Models Object Detection Models Technical requirements Introducing object detection A fast object detection algorithm YOLO Faster R-CNN ? a powerful object detection model Enhancing and Segmenting Images Enhancing and Segmenting Images Technical requirements Transforming images with encoders-decoders Understanding semantic segmentation Training on Complex and Scarce Datasets Training on Complex and Scarce Datasets Technical requirements Efficient data serving How to deal with data scarcity Video and Recurrent Neural Networks Video and Recurrent Neural Networks Technical requirements Introducing RNNs Classifying videos Optimizing Models and Deploying on Mobile Devices Optimizing Models and Deploying on Mobile Devices Technical requirements Optimizing computational and disk footprints On-device machine learning Example app ? recognizing facial expressions

Duration 4 Days 24 CPD hours This course is intended for This course is geared for attendees with Intermediate IT skills who wish to learn Computer Vision with tensor flow 2 Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Working in a hands-on learning environment, led by our Computer Vision expert instructor, students will learn about and explore how to Build, train, and serve your own deep neural networks with TensorFlow 2 and Keras Apply modern solutions to a wide range of applications such as object detection and video analysis Run your models on mobile devices and web pages and improve their performance. Create your own neural networks from scratch Classify images with modern architectures including Inception and ResNet Detect and segment objects in images with YOLO, Mask R-CNN, and U-Net Tackle problems faced when developing self-driving cars and facial emotion recognition systems Boost your application's performance with transfer learning, GANs, and domain adaptation Use recurrent neural networks (RNNs) for video analysis Optimize and deploy your networks on mobile devices and in the browser Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. Hands-On Computervision with TensorFlow 2 is a hands-on course that thoroughly explores TensorFlow 2, the brand-new version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks. This course begins with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface. You'll then move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the course demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U-Net. You will also build generative adversarial networks (GANs) and variational autoencoders (VAEs) to create and edit images, and long short-term memory networks (LSTMs) to analyze videos. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts. Computer Vision and Neural Networks Computer Vision and Neural Networks Technical requirements Computer vision in the wild A brief history of computer vision Getting started with neural networks TensorFlow Basics and Training a Model TensorFlow Basics and Training a Model Technical requirements Getting started with TensorFlow 2 and Keras TensorFlow 2 and Keras in detail The TensorFlow ecosystem Modern Neural Networks Modern Neural Networks Technical requirements Discovering convolutional neural networks Refining the training process Influential Classification Tools Influential Classification Tools Technical requirements Understanding advanced CNN architectures Leveraging transfer learning Object Detection Models Object Detection Models Technical requirements Introducing object detection A fast object detection algorithm ? YOLO Faster R-CNN ? a powerful object detection model Enhancing and Segmenting Images Enhancing and Segmenting Images Technical requirements Transforming images with encoders-decoders Understanding semantic segmentation Training on Complex and Scarce Datasets Training on Complex and Scarce Datasets Technical requirements Efficient data serving How to deal with data scarcity Video and Recurrent Neural Networks Video and Recurrent Neural Networks Technical requirements Introducing RNNs Classifying videos Optimizing Models and Deploying on Mobile Devices Optimizing Models and Deploying on Mobile Devices Technical requirements Optimizing computational and disk footprints On-device machine learning Example app ? recognizing facial expressions