44 Courses delivered Live Online

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

Duration 2 Days 12 CPD hours Overview This skills-focused course combines expert instructor-led discussions with practical hands-on labs that emphasize useful, current techniques, best practices and standards. Working in this hands-on lab environment, guided by our expert practitioner, you'll learn about and explore: Review of the File System Introduction to Shells: sh, bash, and ksh Shell Programming Advanced Shell Features Text Manipulation Utilities File Processing Utilities Multitasking and Batch Processing Regular Expressions Intermediate Linux: Shell, Bash, Text Manipulation, Multitasking & More is a two-day course designed to provide you with hands on experience using standard Linux commands and utilities used for day-to-day tasks including file manipulation, program execution and control, and effective use of the shell and desktop environments. Throughout the course you?ll explore key concepts to Linux core functionality, while learning the system's most commonly used commands. You?ll also learn the Bourne shell, Bash shell and Korn shell programming techniques you?ll need to read and modify existing shell scripts, and create your own. Data manipulation utilities and shell syntax for synthesizing command pipelines are also emphasized throughout the course. Review of the File System File System Organization File Types File and Directory Naming Rules and Conventions Commands for Navigating the File System Introduction to Inodes Ownership, Permissions, and Dates Manipulating Files and Links Manipulating Directories Determining Disk Usage Other File System Utilities Introduction to Shells: sh, bash, and ksh Shell Functions I/O Redirection and Pipes Command Separation and Grouping Background Execution Filename Expansion Shell Variables Command Substitution Quoting and Escaping Metacharacters Bash Shell Features Korn Shell Features Command Execution Startup Files Customizing the User Environment Shell Programming Shell Script Features and Capabilities Creating and Running a Script Working With Variables Environment Variables Working With Data Types Formatting Base Conversion Setting Special Attributes Input/Output Techniques Conditional Constructs if/then else/elif Looping Constructs for, while, until Math Operators Advanced Shell Features Manipulating Strings Writing and Calling Functions Controlling Process Priorities Interpreting Command Line Arguments Making Scripts Interactive Special Shell Variables Advanced I/O with Streams Improving Performance of Scripts Text Manipulation Utilities Editing a File from a Script Scripting with ed or sed UNIX and Linux Utilities to Manipulate Files Regular Expressions grep and egrep The Stream Editor sed Sorting in Scripts Generating Reports with awk Splitting Large Files Counting Words, Lines, and Characters Transforming File Contents File Processing Utilities Examining and Comparing Files Reporting Differences Between Files Comparing Files of Any Format Displaying Data in Octal and Hex Compressing Data Converting File Formats Extracting Text Strings Multitasking and Batch Processing Multitasking Scheduled Execution Using cron The at and batch Commands Regular Expressions Regular Expression Overview Regular Expression Implementations Regular Expressions RE Character Classes Regex Quantifiers RE Parenthesis Additional course details: Nexus Humans Intermediate Linux (TTLX2104) 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 Intermediate Linux (TTLX2104) 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.

Look at the in depth features of charts and some specialist tools to make your charts come to life. Course overview Duration: 1 day (6.5 hours) Our Excel Charting Specialist course examines how to unlock the full potential of Charts in Excel. It investigates the useful tools needed for creating bespoke Charts, allowing you to creatively present data more visually. By the end of the course, you will have an awareness of the tools used for creating more advanced Charts and will be able to create your own Dynamic Charts. The course is designed for existing users of Excel who are looking at more advanced ways to visualise data in Excel using Charts. Objectives By the end of the course you will be able to: Create and Amend Charts Format multiple areas of a Chart Use a Combo Chart Apply Conditional Formatting to a Chart Create Dynamic Charts Work with Custom Formatting Create an Advanced Dynamic Chart Content Creating and amending charts Creating Simple Charts Using the Chart Design tab Adding Chart Elements Changing the type of Chart Formatting charts Format Chart Area Format Data Series Format Data Point Format Axis Working with combo charts Adding additional data to a Chart Customising Chart types Creating Clustered Column/line Charts Applying conditional formatting on charts Using IF Statements Highlighting Max/Min Data points in Charts Using Series Overlap Dynamic charts Creating dynamic Target vs Actual Charts Introducing the NA function Hiding data used for Charts Custom formatting charts Formatting Data Labels Customising number formats Using Trend Arrows to show changes Editing Format Code Advanced dynamic charts Pivot Chart rules Creating a Pivot Chart Working with your Pivot Chart Creating calculations Combining all elements to create an Advanced Chart Using Error Bars to create a line Adding a scroll bar and linking with data