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
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Course Content Introduction Course Outline and Introduction 00:04:00 Minimum Requirements for the Course 00:01:00 Prepayments Introduction 00:01:00 Prepaid Expenses Models: Resources Download Month End Date Prepaid Expenses Amortization Calculation 00:00:00 Exact Prepaid Expenses Payment Date Calculation 00:00:00 Accounting for Prepaid Expenses Prepaid Expenses Accounting Definition: Prepayments 00:03:00 Prepaid Expense Example: How Accounting works for Prepayments 00:03:00 Advantages and Disadvantages of Prepaid Expenses 00:03:00 Excel Formulas Detailed: Introduction to three Excel Models Introduction to PRO Excel Models and Formulas 00:06:00 Date Function 00:05:00 EOMONTH Function 00:04:00 DATEVALUE function 00:03:00 IF Function 00:08:00 IFS Function (Office 365 Only) 00:07:00 VLOOKUP Function 00:07:00 MATCH Function 00:05:00 INDIRECT Function 00:02:00 NAMED Ranges: Name Manager 00:03:00 Advanced Version of VLOOKUP Function 00:07:00 Formula Based Prepaid Expenses Model Introduction to Model and Control Panel Tab (Important Sheet Tab) 00:08:00 Formula Based Prepaid Expenses Model - Deep Dive (Part 1) 00:05:00 Formula Based Prepaid Expenses Model - Deep Dive (Part 2) 00:06:00 Formula Based Prepaid Expenses Model - Deep Dive (Part 3) 00:06:00 IFS Function - Month End date Prepayment calculation 00:04:00 Prepaid Expenses - Closing Balance Summary Tab (Formula Based Summary) 00:09:00 Protecting Formulas Cells and Fields in the Model 00:04:00 Calculate Prepaid Expenses Amortisation from Exact Start Date Exact Date Prepaid Amortisation calculation Intro 00:03:00 Formulas update and Model Changes for Exact Prepaid Exps Calculation 00:03:00 Formulas Update for Exact Date Prepaid Exps Amortisation (Part 1) 00:04:00 Formulas Update for Exact Date Prepaid Exps Amortisation (Part 2) 00:03:00 Formulas Update for Exact Date Prepaid Exps Amortisation (Part 3) 00:02:00 Formulas Update for Exact Date Prepaid Exps Amortisation (Part 4) 00:07:00 IFS Function - Exact Date Prepayments Amortisation 00:04:00 Data Validation Controls (Enhancing Data Input Controls with Protection) 00:10:00 Bonus: Prepayment Model with Opening Balance Calculation (Part 1) 00:08:00 Bonus: Prepayment Model with Opening Balance Calculation (Part 2) 00:09:00 Additional Material: Resources 00:00:00 Prepaid Expenses Summary with Power Query and Pivot Table Power Query and Pivot Table Prepayment Summary Table Intro 00:06:00 What is Power Query and Some Awesome Resources for Power Query learning 00:07:00 Power Query and Pivot Table Summary - Deep Dive (Part 1) 00:05:00 Power Query and Pivot Table Summary - Deep Dive (Part 2) 00:04:00 Power Query and Pivot Table Summary - Deep Dive (Part 3) 00:05:00 Power Query and Pivot Table Summary - Deep Dive (Part 4) 00:09:00 Using Array Formulas to Add Formula Protection 00:04:00 Bonus: Allocate Prepaid Expenditure Cost Centre Wise - 1 00:02:00 Bonus: Allocate Prepaid Expenditure Cost Centre Wise - 2 00:08:00 Bonus: Prepayment Model with Opening Balance Calculation (PQ and PT Version) 00:13:00 Advanced VBA Prepaid Expenses Amortisation Model Changing Macros Security in Excel 00:05:00 Complete Walkthrough - Advanced VBA Prepaid Expenses Amortisation Model 00:06:00 Bonus : New Version - Excel VBA Model for Prepayment Expenditure 00:08:00 BONUS: Dynamic Dashboard for Divisional Profit and Loss statements: Easy Way Dynamic Dashboard Overview 00:07:00 Importing Profit and Loss Statements Source Files and creating YTD P&L Sheets 00:08:00 Creating Dynamic Data Validation 00:02:00 Creating Named Ranges for Dynamic Table Arrays 00:03:00 Dynamic Date Column Headings for each Divisional PL Table 00:02:00 Dynamic Month and YTD Dashboard tables headings (PRO TIP) 00:03:00 Dynamic VLOOKUP Formula - Preparing First section of the Dashboard 00:04:00 Creating Rolling Dashboard with Dynamic VLOOKUP Function 00:08:00 IMPORTANT : Error Checking for your reports/Dashboard (PRO TIP) 00:03:00 Data Prep for Visualization: AREA Charts (Awesome trick using #NA Function) 00:05:00 Visualization: AREA Charts for Month - Revenue, Gross Profit and Net Profit 00:05:00 Visualization DONUT Charts Revenue, Gross Profit and Net Profit (Part 1) 00:03:00 Visualization DONUT Charts Revenue, Gross Profit and Net Profit (Part 2) 00:06:00 Power Query & Pivot Tables based Dashboard without any Formulas, Fully Dynamic Introduction - Formula-less Dashboard - Fully Dynamic and easily refreshed 00:05:00 Understanding the data files before building dashboard 00:02:00 Consolidating Reports with Power Query (Get & Transform) , How to install PQ 00:08:00 Dynamic File Path Trick in Power Query with Parameters (Amazing trick) 00:06:00 Conditional Cumulative totals with SUMIFS Function 00:04:00 Bonus: Conditional Cumulative totals with Power Query Custom Formula (M Code) 00:06:00 Dashboard Creation - Pivot Table showing Month and YTD KPIs division wise 00:06:00 Dashboard Creation Donuts Charts linked with Pivot Table (Replicate Charts fast) 00:08:00 Dashboard Creation - Line Charts 00:08:00 Update Dashboard with Additional Divisional Data with Few Click (Magical) 00:03:00 Thank you Thank you 00:02:00 Ultimate Prepaid Expenditure Model (Super Bonus) 00:02:00 Resources Resources - Microsoft Excel: Automated Dashboard Using Advanced Formula, VBA, Power Query 00:00:00 Frequently Asked Questions Are there any prerequisites for taking the course? There are no specific prerequisites for this course, nor are there any formal entry requirements. All you need is an internet connection, a good understanding of English and a passion for learning for this course. Can I access the course at any time, or is there a set schedule? You have the flexibility to access the course at any time that suits your schedule. Our courses are self-paced, allowing you to study at your own pace and convenience. How long will I have access to the course? For this course, you will have access to the course materials for 1 year only. This means you can review the content as often as you like within the year, even after you've completed the course. However, if you buy Lifetime Access for the course, you will be able to access the course for a lifetime. Is there a certificate of completion provided after completing the course? Yes, upon successfully completing the course, you will receive a certificate of completion. This certificate can be a valuable addition to your professional portfolio and can be shared on your various social networks. Can I switch courses or get a refund if I'm not satisfied with the course? We want you to have a positive learning experience. If you're not satisfied with the course, you can request a course transfer or refund within 14 days of the initial purchase. How do I track my progress in the course? Our platform provides tracking tools and progress indicators for each course. You can monitor your progress, completed lessons, and assessments through your learner dashboard for the course. What if I have technical issues or difficulties with the course? If you encounter technical issues or content-related difficulties with the course, our support team is available to assist you. You can reach out to them for prompt resolution.