Learn YOLOv4 Object Detection | OpenCV Python | Computer Vision (2021)" https://www.youtube.com/watch?v=Iw3tducClFw --~-- Did you know it is possible to playback video in your Augmented Reality Apps? This Unity 2020 Tutorial will teach you how to implement video playback in Vuforia 8. ▶Vuforia AR Course http://bit.ly/Vuforia80 ▶Internet Of Things in Augmented Reality (IoTAR) http://bit.ly/IoTAR80 ▶ARCore MasterClass http://bit.ly/ARCORE80 ------------------------------------------------------------ ⭐ Kite is a free AI-powered coding assistant that will help you code faster and smarter. The Kite plugin integrates with all the top editors and IDEs to give you smart completions and documentation while you’re typing. I've been using Kite for a few months and I love it! http://bit.ly/KITEAUTOCOMPLETE ------------------------------------------------------------ Hey guys and welcome back. So this quick lecture, Im going to show you how you can have video playback functionality in your Augmented Reality App using Vuforia 8, and Unity 2020. Applications of this are, for an AR business card where you showcase your talents and skills, you can also use this for Educational AR Apps and for displaying video instructions So the reason why we are redoing this video again, is because of a number of a number reasons. 1. My last video I did not show how to deploy to Android, it was a PC only tutorial 2. Separate Audio and video were separate so I only showed how to display video without simultaneous audio playback. 3. There was a lot of Bugs in the video playscript and you couldn’t play common video formats easily like .mp4, only .avi at the time. 4. And lastly, that video is heavy outdated. I used Vuforia 6 in Unity 5.3. Its definitely time for a fresh updated Unity 2020 tutorial on video playback. So Im really excited to tell you that this video will addresses all of the aforementioned issues in Unity 2020, the entire process has had a massive revamped. So instead of taking you 15 minutes to get this functionality working, we will do it all in under 5 minutes, Saving you a lot of time and effort. If there’s any other features or AR Application that you would like for me to cover, please comment down below. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy Follow us on Twitter ►Augmentedstartups.info/Twitter ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented ... https://www.youtube.com/watch?v=PyBDJvs0y1Q

Today we are going figure out how Linear Discriminant Analysis (LDA) and why its so useful! ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS Course - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML Limited Time - Discount Coupon Linear Discriminant Analysis (LDA) is most commonly used as dimensionality reduction technique in the pre-processing step for pattern-classification and machine learning applications. The goal is to project a dataset onto a lower-dimensional space with good class-separability in order avoid over-fitting (“curse of dimensionality”) and also reduce computational costs. Ronald A. Fisher formulated the Linear Discriminant in 1936, and it also has some practical uses as classifier. The original Linear discriminant or Fisher Linear Discriminant Analysis was described for a 2-class problem, and it was then later generalized as “multi-class Linear Discriminant Analysis” or “Multiple Discriminant Analysis” by C. R. Rao in 1948 for his research “The utilization of multiple measurements in problems of biological classification” Please Subscribe and click that bell icon to join our notification squad! There are many possible techniques for classification of data. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two commonly used techniques for data classification and dimensionality reduction. PCA finds the most accurate data representation in a lower dimensional space. It projects data in the directions of maximum variance. However the directions of maximum variance may be not be useful for classification. https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML Limited Time - Discount Coupon Fisher’s Linear Discriminant Analysis or just LDA projects to a line which it inherently preserves direction useful for data classification. The main idea of LDA is to find projection to a line so that samples from different classes are well separated. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :) ... https://www.youtube.com/watch?v=2cngQxtbkDc