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Free Download Applied Yocto Project using Raspberry Pi 5 (Embedded Linux) Published 10/2024 Created by Mustafa Ozcelikors MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 27 Lectures ( 10h 54m ) | Size: 6.1 GB Explore Embedded Linux using Yocto Project and create powerful systems on Raspberry Pi 5 What you'll learn: Understand the core philosophy behind Embedded Linux systems from Introductory to Advanced subjects. Understand why we use Embedded Linux in certain products. Understand how Linux systems and Raspberry Pi 5 boot. Grasp how embedded Linux tasks work (unpack, patch, configure, compile, install, deploy, package). Understand Linux distributions and package managers. Comfortably handle tasks regarding Yocto Project and its configuration with ease. Understand how Raspberry Pi 5 layer and board configuration work in Yocto Project (meta-raspberrypi, hardware specs, serial console). Create your own machine configurations within Yocto Project. Create your own layers for your Linux distribution. Comfortably create new recipes for software packages that use multiple build systems (GCC, GNU Make, CMake, PyPi). Integrate third party software (e.g. GNOME, XFCE, Qt) in Embedded Linux products. Integrate graphical desktop in your Embedded Linux system. Deploy system images to Raspberry Pi 5 and work with secure shells and serial console software. Create patches for existing software packages using DEVTOOL. Understand how to comfortably understand and modify kernel configurations (Kconfig, config fragments). Downgrade and upgrade Linux Kernel version of your embedded Linux distribution. Requirements: Basic understanding of Linux systems (shell scripts, building software) Basic understanding of C programming Description: Hi,My name is Mustafa Ozcelikors, a Senior Linux & Android Engineer and team leader with more than +8 years of experience working in automotive with Master's Degree in Embedded Systems, who have won Google SOC event in 2017 and who have been actively contributing to open source and commercial projects. I have been using Yocto Project for almost 10 years, dating back to my college years."Applied Yocto Project using Raspberry Pi 5 (with Embedded Linux practices)" is an unique Embedded Linux course unlike any other. The course utilizes drawings, slideshows, diagrams, examples, hands-on applications in order for you to visualize every subject in your mind with ease. Course level starts with beginner, but some advanced topics are also explained.Theoretical subjects are carefully planned and almost for every important subject, multiple block diagrams have been created for better understanding. Almost every figure, schematic, drawing comes from years of experience in the field, not from the internet or from any book. In practical subjects, we take a deep dive in exploring Yocto Project together, and open up a terminal together to achieve things within it.At the end of the course we together will have a custom Linux distribution with distinct machine configuration, kernel, recipe examples, and Qt application.You are in this now for the following great content:Understand the core philosophy behind Embedded Linux systems from Introductory to Advanced subjects.Understand why we use Embedded Linux in certain products.Understand how Linux systems and Raspberry Pi 5 boot.Grasp how embedded Linux tasks work (unpack, patch, configure, compile, install, deploy, package).Understand Linux distributions and package managers.Comfortably handle tasks regarding Yocto Project and its configuration with ease.Understand how Raspberry Pi 5 layer and board configuration work in Yocto Project (meta-raspberrypi, hardware specs, serial console).Create your own machine configurations within Yocto Project.Create your own layers for your Linux distribution.Comfortably create new recipes for software packages that use multiple build systems (GCC, GNU Make, CMake, PyPi).Integrate third party software (e.g. GNOME, XFCE, Qt) in Embedded Linux products.Integrate graphical desktop in your Embedded Linux system.Deploy system images to Raspberry Pi 5 and work with secure shells and serial console software.Create patches for existing software packages using DEVTOOL.Understand how to comfortably understand and modify kernel configurations (Kconfig, config fragments).Downgrade and upgrade Linux Kernel version of your embedded Linux distribution.Thank you very much for your interest! I hope to see you in the first lecture!Kindly yours,M.Eng Mustafa Ozcelikors Who this course is for: Embedded Linux professionals who want to get started with Yocto Project Embedded Linux professionals who appreciate a great wrap-up Aspiring embedded system developers who would like to start with Embedded Linux development Hobbyists that would like to create Raspberry Pi 5 based Embedded Linux products Homepage https://www.udemy.com/course/yocto-project/ Rapidgator https://rg.to/file/03ab973c52f596b82812e4562aaf8ae4/rmwuq.Applied.Yocto.Project.using.Raspberry.Pi.5.Embedded.Linux.part6.rar.html https://rg.to/file/290119d18ee15f7ba684525ff65496f0/rmwuq.Applied.Yocto.Project.using.Raspberry.Pi.5.Embedded.Linux.part2.rar.html https://rg.to/file/29a4b90bbe6515d15544f1892abd7ffe/rmwuq.Applied.Yocto.Project.using.Raspberry.Pi.5.Embedded.Linux.part4.rar.html https://rg.to/file/70d6131e1d73d2b30f9ff7511f14705c/rmwuq.Applied.Yocto.Project.using.Raspberry.Pi.5.Embedded.Linux.part5.rar.html https://rg.to/file/b1907dc7bbbd6c885270677747df1ff0/rmwuq.Applied.Yocto.Project.using.Raspberry.Pi.5.Embedded.Linux.part3.rar.html https://rg.to/file/bdc5c8524e33ef50e408fd41a39a6a15/rmwuq.Applied.Yocto.Project.using.Raspberry.Pi.5.Embedded.Linux.part7.rar.html https://rg.to/file/e690b8719af92b37ea42788c2f2866da/rmwuq.Applied.Yocto.Project.using.Raspberry.Pi.5.Embedded.Linux.part1.rar.html Fikper Free Download https://fikper.com/JWgfnmx9W8/rmwuq.Applied.Yocto.Project.using.Raspberry.Pi.5.Embedded.Linux.part2.rar.html https://fikper.com/davtFclPlW/rmwuq.Applied.Yocto.Project.using.Raspberry.Pi.5.Embedded.Linux.part7.rar.html https://fikper.com/lQZWZZWrMi/rmwuq.Applied.Yocto.Project.using.Raspberry.Pi.5.Embedded.Linux.part6.rar.html https://fikper.com/lbpYqtBKL2/rmwuq.Applied.Yocto.Project.using.Raspberry.Pi.5.Embedded.Linux.part5.rar.html https://fikper.com/lcCXKSOyB5/rmwuq.Applied.Yocto.Project.using.Raspberry.Pi.5.Embedded.Linux.part1.rar.html https://fikper.com/mhYRMJXYtK/rmwuq.Applied.Yocto.Project.using.Raspberry.Pi.5.Embedded.Linux.part4.rar.html https://fikper.com/oQ5rWKWvmK/rmwuq.Applied.Yocto.Project.using.Raspberry.Pi.5.Embedded.Linux.part3.rar.html No Password - Links are Interchangeable

Free Download Applied Computer Vision Object Detection and Recognition Published 10/2024 Created by Vahid Mirjalili, PhD,Taban Eslami MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 44 Lectures ( 2h 14m ) | Size: 853 MB Object Recognition From Basics to Advanced Techniques What you'll learnUnderstand the fundamentals of image recognition, including image classification, object detection, and image segmentation (semantic, instance, and panoptic)) Master the underlying theories and principles of key computer vision models, enabling a deep understanding of their functionality and applications Master PyTorch fundamentals, learn how to build a CNN model, and your custom image dataset Implement advanced image recognition models and train them in PyTorch RequirementsPython Programming Experience: Familiarity with programming, particularly in Python, as it's the primary language used with PyTorch. Students should be comfortable with basic programming concepts and structures. Understanding of Basic Machine Learning Concepts: A foundational knowledge of machine learning principles, including what models are, how they are trained, and a basic understanding of concepts like classification, regression, overfitting, and underfitting. Introductory Knowledge of Deep Learning: Familiarity with the basic concepts of neural networks, including what they are and how they are generally structured and trained. DescriptionComputer Vision and Object RecognitionThis course provides a comprehensive journey into computer vision and object recognition, guiding you from the foundational concepts to advanced model implementation and evaluation. Through a hands-on approach, you will explore key computer vision tasks such as image classification, object detection, semantic segmentation, and instance segmentation. The course uses popular datasets like COCO-2017 and CamVid, and frameworks such as PyTorch and FiftyOne to enhance your practical skills.Section 1: Introduction We begin with an overview of the course and object recognition, followed by setting up the necessary environment for efficient implementation.Section 2: Recap of Convolutional Neural Networks (CNNs) This section refreshes your knowledge of CNNs and introduces essential tools like FiftyOne for dataset management, along with tutorials to get familiar with PyTorch.Section 3: Image Classification You will learn to build and train a multi-class image classifier using the COCO-2017 dataset, focusing on classes like cats, dogs, and horses. The classifier is built using a pre-trained ResNet model, demonstrating the process of transfer learning and hyperparameter tuning.Section 4: Object Detection We delve into object detection using two popular models, Faster-RCNN and YOLOv8. You'll prepare datasets, train both models, and analyze their performance using FiftyOne, gaining hands-on experience with both region-based and single-shot detection methods.Section 5: Semantic Segmentation In this section, you will work with the CamVid dataset to understand semantic segmentation, which involves assigning a class to every pixel in an image. Using the segmentation_models_pytorchlibrary, you will train and evaluate a segmentation model to recognize objects in scenes.Section 6: Instance Segmentation We cover instance segmentation, where the goal is to differentiate between multiple instances of the same object class. You'll build and train a Mask-RCNN model for this task, working with segmentation annotations from the COCO-2017 dataset.Throughout the course, we place a strong emphasis on hands-on exercises, real-world datasets, and model evaluation to equip you with the skills needed to tackle practical computer vision challenges. By the end, you will be well-prepared to implement and evaluate various computer vision models, with a solid understanding of the nuances involved in different tasks like classification, detection, and segmentation. Who this course is forStudents and Learners in Computer Science: Undergraduate or graduate students who are majoring in computer science, data science, artificial intelligence, or related fields and want to gain practical skills in image recognition using PyTorch. Aspiring Data Scientists and Machine Learning Engineers: Individuals looking to enter the field of data science or machine learning with a specific interest in image processing and recognition techniques. AI and Machine Learning Enthusiasts: Individuals who have a keen interest in artificial intelligence and machine learning and want to deepen their understanding of image recognition. Tech Entrepreneurs: Entrepreneurs or innovators looking to understand image recognition to implement or improve product offerings, particularly in tech-driven markets. Homepage https://www.udemy.com/course/applied-computer-vision/ Rapidgator https://rg.to/file/46055b26242e55dd14d3d3bebc61a186/cqinv.Applied.Computer.Vision.Object.Detection.and.Recognition.rar.html Fikper Free Download https://fikper.com/6THTR8RTbL/cqinv.Applied.Computer.Vision.Object.Detection.and.Recognition.rar.html No Password - Links are Interchangeable

Free Download Applied Bayes' Theorem And Naive Bayes Classifiers Published 10/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 3.49 GB | Duration: 14h 7m Learn the fundamentals to better develop or acquire such Machine Learning Methods What you'll learn Detailed and fundamental probability principles, rules and procedures The Confused Matrix and its KPI's to be used as an evaluation of Naïve Bayes Classifier The Variant of Confused Matrices when datasets have multiple labels in their class or multiple classes The theory and principles behind the Bayes' Theorem How to develop the inference procedures in Bayes' Theorem using vertical and horizontal tables, contingency tables and decision trees The theoretical basis of Naïve Bayes Classifier Categorical Naïve Bayes Classifiers Laplace Smoothing Correction and M-Estimates Continuous Naïve Bayes Classifiers based on Gaussian distributions Continuous Naïve Bayes Classifiers based on non-Gaussian distributions (in this case, the Beta Distribution) The Beta Distribution and how to derive its parameters from our data The four discrete distributions in use in the Bayes' Theorem: Categorical, Bernoulli, Binomial and Multinomial Bernoulli Naïve Bayes Classifiers Multinomial Naïve Bayes Classifiers Weighted Naïve Bayes Classifiers Complemented Naïve Bayes Classifiers Entropy and Information Gain for better classification The Kononenko Information Gain and its application in Naïve Bayes Classifiers The Log Odds Ratio and its application in Naïve Bayes Classifiers The Kernel Density Estimates and its application in Naïve Bayes Classifier The optimization of the bandwidth, h, in the Kernel Density Estimates Requirements A working knowledge of Excel A beginner's knowledge of VBA (in Excel) No Python or R are needed All statistical methods will be presented in the course Description A) The Purpose of the CourseMost courses on this subject are aimed at Machine Learning and Data Science experts. Often, they are presented for use with specialized development platforms or even as part of advanced off-the-shelf applications. On the other hand, the Bayes' Theorem and its applications are based on statistical principles and concept not often clearly explained.The purpose of this course is educational. The techniques, algorithms and procedures presented in this course aim more at making machine learning methods based on the Theorem easier to understand as opposed to getting used.The Bayes' Theorem is is one of those theorems where we can apply the proverb: "Still water is deep". The Theorem was developed in an article by Thomas Bayes in 1763. In due course, it found itself being used in a wide variety of statistical applications. The Theorem itself was an application of inference. From there on, and specifically with the advent of Machine Learning algorithms, the Theorem was extended to be the core of a wide variety of applications such as Classification, Networks and Optimization.The Theorem and its applications are best developed using specialized programming environments. This is due to the mere fact that the applications of the Theorem require the handling of large data and performance intensive environments.B) So, why do we Present a Course based on Excel?Analysts require the use and the development of such applications have the following environments available to them:· Off the shelf applications, ready-made and commercially available.· Open source or free integrated development environments (IDE) that host a large number of scientific and statistical libraries to use in such applicationsIn both cases, the Analyst is faced with an insurmountable learning curve, often not climbable at all. Whether the objective is to use off-the-shelf products or to develop their own applications, learning the methods in a machine learning environment is not possible via these two environments.The course will then use Excel specifically for educational purposes and not as a machine learning tool. Excel is known by everyone, and if not, it is easy to learn. Excel is highly flexible in terms of exposing how things work. The course will then exploit such facilities to expose to the Analyst in a common sense and step-by-step manner the basis and procedures of these algorithms.B) What Does the Course Cover?The course is made up of 5 major sections preceded by a short introduction.Section 1: Introducing the CourseThis section consists of one lecture that presents the objectives of the course, its structure and resources as well as what to expect and what not to expect.Section 2: An In-Depth Presentation of Probability Rules and PracticesThe section starts with lectures that run through a detailed exposure to the fundamentals and practices of probability rules. Bayes' Theorem is highly linked with such rules and it will not be possible for analysts embarking on its use (and the understanding of its extensions) to learn and use these algorithms without a deep understanding of probability.The section uses common sense to clarify often obscure concepts in probability. Many examples are presented and explained in detail.Section 3: The Use of the Confusion Matrix for Evaluating Bayesian ResultsSome might wonder why we are introducing the Confusion Matrix and its useful KPI's in this course. The answer is that in both Sections 3 and 4, we will need to evaluate our results in terms of precision, accuracy, error rates, etc. The Confusion Matrix is a contingency table consisting of four results extracted from comparing the algorithm's outcome with the historically known outcome of the classes in a Test Table. Four measurements consist of True Positive, True Negative, False Positive and False Negative. These four counts can be used in a variety of ways to measure such KPI's as accuracy, precision, error rates and such. (The confusion matrix is also used in a variety of other classification machine learning methods: logistic regression, decision trees, etc.)Section 4: The Fundamental Application of Bayes' Theoremthis section presents the Theorem of Bayes first running through a common-sense example. This is followed by the derivation of the Theorem and a clear explanation of the terms used in the Bayes' Theorem formula. A set of 8 major workouts present the use of the Theorem in different formats (vertical and horizontal tables, decision trees and graphic solutions). The last 3 workouts output the results of the workouts to a Confusion Matrix and shows how that can be used to evaluate the results of the Theorem.Section 5: How to Use the Naïve Bayes Classifiersthis is the heart of the course. It presents a wide variety of algorithms whose purpose is the supervised classification of data. The Naïve Bayes Classifiers are a family of algorithms based on the Bayes' Theorem. They differ in various ways from each other. They are listed below.Amongst the lectures detailing these algorithms with clear examples are "support" lectures that present topics that are needed as a support to these algorithms.After starting with two lectures that present the fundamentals of Naïve Bayes Classifiers and the required theory, the course proceeds with a set of lectures consisting of 8 Naïve Bayes Classifier variants:1) Categorical Naïve Bayes Classifiers2) Gaussian and Continuous Naïve Bayes Classifiers3) Non-Gaussian Continuous Naïve Bayes Classifiers4) Bernoulli Naïve Bayes Classifier5) Multinomial Naïve Bayes Classifier6) Weighted Naive Bayes Classification7) Complement Naïve Bayes Classification8) Kernel Distance Estimation and Naive Bayes ClassificationTo support the presentations above, the course will interleave the following detailed presentations consisting of methods, topics and procedures:1) Laplace Smoothing Correction2) Extensions to Continuous Features: checking for normality, checking for independence of features, smoothing corrections for Gaussian features3) Two Discrete Distributions - Bernoulli and Categorical4) Two Discrete Distributions - Binomial and Multinomial5) Entropy and Information and how used in Naïve Bayes Classification6) Kononenko Information Gain and Evaluation of Classifiers7) Log Odds Ratio and Nomograms used in Bayes Classification8) Kernel Distance Estimation - Estimating the Bandwidth hResourcesAll lectures will be supported by a variety of resources:· Solved and documented workouts in Excel· Dedicated workbooks that animate and describe various probability distributions· Links to Interesting articles and books Overview Section 1: Introduction Lecture 1 The Purpose and Structure of this Course Section 2: An In-Depth Presentation of Probability Rules and Practices Lecture 2 Fundamental Definitions of Probability Lecture 3 Probability - Distribution Tables (Contingency) Lecture 4 Probability Rule 1) Intersection Rule for Independent Events (JOIN) Lecture 5 Probability Rule 2) The Union Rule (OR or UNION) Lecture 6 Probability Rule 3) The Union Rule (XOR or Exclusive UNION) Lecture 7 Probability Rule 4) The Intersection Rule (AND or CONDITIONAL) for Dependent Eve Lecture 8 Probability Rule 4) The Intersection Rule for Dependent Events (Examples) Lecture 9 Probability - Decision Trees and Probability Lecture 10 Probability Rule 5) The Meaning of Independence and Mutual Exclusivity Lecture 11 Probability Rule 6) Total Probability Lecture 12 Probability Rule 7) The Chain Rule of Probability Section 3: The Use of the Confusion Matrix for Evaluating Bayesian Results Lecture 13 Evaluating Classifiers with the Confusion Matrix and its KPIs Lecture 14 Extracting KPI's from the Confusion Matrix Lecture 15 Evaluating Classifiers in the Case of Multiple Classes or Multiple Labels Section 4: The Fundamental Application of Bayes' Theorem Lecture 16 Bayes Theorem - Rationale and Derivation of Theorem Lecture 17 Bayes Theorem - A Bayesian Story, an Example without Formulas Lecture 18 Bayes Theorem - Defining the Factors in Bayes' Theorem Lecture 19 Bayes' Theorem - (W1) The Famous HIV Test Lecture 20 Bayes' Theorem - (W2-W4) Spam Testing 3 Events, Defective Machines and HIV (Hori Lecture 21 Bayes' Theorem - (W5) Spam Testing (Contingency Table and Graphic Solutions) Lecture 22 Bayes' Theorem - (W5) Spam Testing (Continued - Deriving Posteriors with the Con Lecture 23 Bayes' Theorem - (W6-W8) Predicting Rain and Identifying Product Suppliers + Mon Section 5: Naive Bayes Classifiers Lecture 24 Naive Bayes Classifiers - Introduction Lecture 25 Naive Bayes Classifiers - Introducing the Algorithm Lecture 26 Naive Bayes Classifiers - The Algorithm thru a Short Example Lecture 27 Naive Bayes Classifiers - The Naive Bayes Classifier Procedure Applied on a Cate Lecture 28 Naive Bayes Classifiers - More Categorical Examples Lecture 29 Naive Bayes Classifiers - Laplace Smoothing Correction Lecture 30 Naive Bayes Classifiers - Correction using M-Estimates Lecture 31 Naive Bayes Classifiers - Gaussian and Continuous Lecture 32 Naive Bayes Classifiers - Three Gaussian Examples Lecture 33 Naive Bayes Classifiers - Some Extensions to Continuous Features Lecture 34 Naive Bayes Classifiers - Handling Non-Gaussian Continuous Features (Beta Distri Lecture 35 Naive Bayes Classifiers - Applying the Non-Gaussian (Beta) Procedure to Personal Lecture 36 Naive Bayes Classifiers - Discrete Distributions - Bernoulli and Categorical Lecture 37 Naive Bayes Classifiers - Discrete Distribution - Binomial Lecture 38 Naive Bayes Classifiers - Discrete Distribution - Multinomial Lecture 39 Naive Bayes Classifiers - Bernoulli Naive Bayes Examples Lecture 40 Naive Bayes Classifiers - Multinomial Naive Bayes Examples Lecture 41 Naive Bayes Classifiers - Weighted Naive Bayes Lecture 42 Naive Bayes Classifiers - Complement Classifier Lecture 43 Naive Bayes Classifiers - Entropy and Information Lecture 44 Naive Bayes Classifiers - Kononenko Information Gain and Evaluation Feature Infl Lecture 45 Naive Bayes Classifiers - Log Odds Ratio and Nomograms Lecture 46 Naive Bayes Classifiers - Kernel Distance Estimation (Discrete and Continuous Di Lecture 47 Naive Bayes Classifiers - Kernel Distance Estimation and Application of Naive Ba Lecture 48 Naive Bayes Classifiers - Kernel Distance Estimation - Estimating the Bandwidth Data Scientists and Analysts,Machine Learning Engineers,Artificial Intelligence Researchers,Software Developers,Business Analysts,Market, Healthcare, Education and Financial professions,Cybersecurity Experts,Natural Language Processing (NLP) Specialists,Product Managers,Business Improvement Experts,Quality Assurance Professionals Screenshot Homepage https://www.udemy.com/course/applied-bayes-theorem-and-naive-bayes-classifiers/ Rapidgator https://rg.to/file/68b69b902e73cc1bf284183d5a2d8126/gwwth.Applied.Bayes.Theorem.And.Naive.Bayes.Classifiers.part1.rar.html https://rg.to/file/68bd7496b68c9b47eebfc2f8b43a4da4/gwwth.Applied.Bayes.Theorem.And.Naive.Bayes.Classifiers.part2.rar.html https://rg.to/file/6dc74f70d5f45951d3243b309154c6fd/gwwth.Applied.Bayes.Theorem.And.Naive.Bayes.Classifiers.part3.rar.html https://rg.to/file/b68ae0c8255564a270eac8df3d8378b1/gwwth.Applied.Bayes.Theorem.And.Naive.Bayes.Classifiers.part4.rar.html Fikper Free Download https://fikper.com/1pZNayBof2/gwwth.Applied.Bayes.Theorem.And.Naive.Bayes.Classifiers.part2.rar.html https://fikper.com/4tjlHz81za/gwwth.Applied.Bayes.Theorem.And.Naive.Bayes.Classifiers.part3.rar.html https://fikper.com/nsdDGIetOO/gwwth.Applied.Bayes.Theorem.And.Naive.Bayes.Classifiers.part4.rar.html https://fikper.com/u6bGGzlymp/gwwth.Applied.Bayes.Theorem.And.Naive.Bayes.Classifiers.part1.rar.html No Password - 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Free Download OpenSite Designer - AI Applied to Terrain Design - AulaGEO Published 9/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 54m | Size: 652 MB Master terrain design with OpenSite Designer, using AI tools to optimize site layouts and grading efficiently What you'll learn How to get started with OpenSite Designer and set up your project files. Navigation and manipulation of 2D and 3D views within the platform. Define the Geographic Coordinate System (GCS) and work with terrain models. Site layout design, including defining site boundaries, building footprints, and creating parking lots. Reviewing and optimizing site designs using AI-powered features. Requirements Basic concepts of surveying Description Course Objective: This course aims to equip learners with practical knowledge of OpenSite Designer, focusing on the use of artificial intelligence (AI) for terrain design. Through a series of hands-on exercises, students will develop skills in creating and managing terrain models, optimizing site layouts, and leveraging AI tools to streamline site design processes.What You Will Learn:How to get started with OpenSite Designer and set up your project files.Navigation and manipulation of 2D and 3D views within the platform.Define the Geographic Coordinate System (GCS) and work with terrain models.Site layout design, including defining site boundaries, building footprints, and creating parking lots.Connection design for driveways and access points.Advanced grading techniques for project sites.Reviewing and optimizing site designs using AI-powered features.Course Content: #AulaGEOExercise OverviewGetting StartedNavigationDefine GCSTerrain ModelWorking File Setup2D and 3D ViewsSite BoundaryBuilding FootprintMain DriveParking LotsReview SiteDrive ConnectionProject GradingWho This Course Is For:Civil engineers, architects, and land developers looking to improve their terrain design skills.Professionals and students interested in AI applications in site design and layout.Anyone wanting to enhance their knowledge of OpenSite Designer for terrain modeling and site planning. Who this course is for Civil engineers, architects, and land developers looking to improve their terrain design skills. Professionals and students interested in AI applications in site design and layout. Anyone wanting to enhance their knowledge of OpenSite Designer for terrain modeling and site planning. Homepage https://www.udemy.com/course/opensite-designer-ai-applied-to-terrain-design-aulageo/ Rapidgator https://rg.to/file/4d247027ac6e4222d1b094f367742c34/trcrq.OpenSite.Designer.AI.Applied.to.Terrain.Design..AulaGEO.rar.html Fikper Free Download https://fikper.com/6HjK7MxWU2/trcrq.OpenSite.Designer.AI.Applied.to.Terrain.Design..AulaGEO.rar.html No Password - Links are Interchangeable

Free Download ACT and Applied Behavior Analysis: A Practical Guide to Ensuring Better Behavior Outcomes Using Acceptance and Commitment Training by Thomas G. Szabo BCBA-D, Jonathan Tarbox - Foreword by BCBA-D, Asa Siegel English | March 19, 2024 | ISBN: B0CX3DFKPL | 12 hours and 55 minutes | PDF | 387 Mb As a board-certified behavior analyst (BCBA), you work with a wide range of clients, particularly those with autism spectrum disorder (ASD). Some of these clients may not be verbal at all on one end of the spectrum, while some may have very advanced language skills on the other. For these clients and their families, you need a flexible and adaptable therapeutic framework to ensure the best behavior outcomes. Drawn from relational frame theory (RFT), acceptance and commitment therapy (ACT) can help. With this definitive professional manual, you'll learn to conceptualize your cases using ACT, create your own exercises, generate metaphors, and practice the core ACT skills flexibly to ensure better behavior outcomes for clients and their families. You'll find an overview of the theoretical connections between behavior analysis, RFT, and ACT, as well as the core act skills, including present-moment awareness, flexible perspective taking, committed action, and values work. Finally, you'll find information on cultural competency and diversity to help you service a wide range of clients. If you're like many BCBAs, you need specialized resources when working with linguistically sophisticated clients, as well as their parents and caregivers. Let this book be your comprehensive guide to incorporating ACT into your work. Rapidgator https://rg.to/file/cefbef00889541be42851a265e68521a/r8n1a.ACT.and.Applied.Behavior.Analysis.A.Practical.Guide.to.Ensuring.Better.Behavior.Outcomes.Using.Acceptance.zip.html Fikper Free Download https://fikper.com/JjiF9JStlf/r8n1a.ACT.and.Applied.Behavior.Analysis.A.Practical.Guide.to.Ensuring.Better.Behavior.Outcomes.Using.Acceptance.zip.html Links are Interchangeable - No Password - Single Extraction

Free Download Applied Life Cycle Cost Analysis (Lcc) Published 9/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 240.69 MB | Duration: 0h 33m Part of Life Cycle Management course What you'll learn Comprehensive Life Cycle Cost (LCC) Analysis Application of LCC in Decision-Making LCC Modeling and Sensitivity Analysis Integration of RAM and ILS with LCC Requirements No prior knowledge is needed. Description This mockup is part of the Life Cycle Management course package, which aims to provide professionals with a comprehensive understanding of managing the total cost and performance of systems or products throughout their lifecycle. The course is grounded in key areas such as Life Cycle Cost (LCC) analysis. It equips parti[beeep]nts with the skills necessary to make informed decisions that minimize costs, improve system reliability, and ensure long-term operational effectiveness.The course covers both theoretical concepts and practical applications, enabling parti[beeep]nts to develop and refine their expertise in lifecycle management. Through real-world case studies, mock assignments, and practical exercises-like the mockup provided here-learners will gain hands-on experience. This mockup, in particular, challenges parti[beeep]nts to apply LCC principles in a decision-making process, such as selecting a cost-effective motor for a fighter jet, by evaluating factors like acquisition costs, reliability, and maintenance needs.Assignments like these are crucial to the course because they allow parti[beeep]nts to:Build and analyze LCC models to understand long-term cost implications.Perform sensitivity analysis to assess the impact of uncertainties.Make strategic recommendations based on data-driven insights.As part of the larger Life Cycle Management course package, this mockup and other exercises are designed to provide a solid foundation in lifecycle management. Whether you are selecting components for procurement, optimizing maintenance plans, or managing system upgrades, the knowledge gained through this course will enable you to make effective decisions that balance cost, performance, and reliability over the product's entire lifecycle.By the end of the course, parti[beeep]nts will be equipped to reduce total lifecycle costs, improve operational outcomes, and contribute to the strategic success of their organizations. Overview Section 1: Mockup 1: Applied Life Cycle Cost Analysis (LCC) - Introduction Lecture 1 Introduction Section 2: Mockup 1: Applied Life Cycle Cost Analysis (LCC) - LCC modeling Lecture 2 LCC modeling Section 3: Mockup 1: Applied Life Cycle Cost Analysis (LCC) - Case study Lecture 3 Case study Section 4: Quiz and Reading time! Lecture 4 Reading - LCCbased approach for design and requirement specifcation for railway Section 5: Assignment - Selecting the Most Cost-Effective Motor for a Fighter Jet Section 6: Mockup 1: Applied Life Cycle Cost Analysis (LCC) - Guidelines and summery Lecture 5 Guidelines and summery This course is designed for beginners in the field of Life Cycle Cost (LCC) analysis and related areas such as Integrated Logistics Support (ILS) and Reliability, Availability, Maintainability (RAM). It is ideal for:,New professionals entering roles in industries like transportation, infrastructure, and engineering, who need to understand LCC as a tool for optimizing costs over the lifespan of products and systems.,Procurement and project managers who are new to LCC and want to make informed, cost-effective decisions for procurement, maintenance, and system development.,Individuals with no prior experience in LCC but with a keen interest in learning how it can be applied to improve cost efficiency, reliability, and operational performance across various industries.,This course provides the essential tools, concepts, and practical insights to help beginners get started in the area of LCC. Homepage https://www.udemy.com/course/applied-life-cycle-cost-analysis-lcc/ Rapidgator https://rg.to/file/baf81bc3082ff200c4192f40ffa0d47c/zvxyj.Applied.Life.Cycle.Cost.Analysis.Lcc.rar.html Fikper Free Download https://fikper.com/Wt1Cec11wo/zvxyj.Applied.Life.Cycle.Cost.Analysis.Lcc.rar.html No Password - Links are Interchangeable

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