August 16 2023

Learn Computer Vision With Four End-End Projects


Learn Computer Vision With Four End-End Projects
Learn Computer Vision With Four End-End Projects
Published 8/2023
Created by Muhammad Talha
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 5 Lectures ( 3h 26m ) | Size: 1.93 GB


Four End to End projects to learn YOLO, ResNet and Classification Model for Computer Vision

What you'll learn
Computer Vision For Beginners
Image Classification Using ResNets
Object Detection
Custom Dataset Training YOLO Family (YOLOv5 and YOLOv8)

Requirements
Students should have a basic knowledge of Python and Computer Vision. Even though this course is very beginner friendly, however students should be familiar with the concepts of Image classification, Object Detection etc.

Description
Unleash the power of Computer Vision with this comprehensive, project-based course designed specifically for beginners. By the end of this course, you'll have a solid understanding of essential Computer Vision models such as YOLO (You Only Look Once) and ResNets, as well as practical experience in implementing these models in real-world scenarios.Whether you're an aspiring data scientist, a software engineer looking to diversify your skill set, or simply a tech enthusiast eager to delve into the exciting field of Computer Vision, this course has got you covered. No prior experience is needed—we start with the basics and work our way up!Our meticulously crafted projects will help you grasp the fundamentals, give you hands-on practice, and imbue you with the confidence to apply these models in your own projects or work. The course features four main projects, each tailored to help you master a different aspect of Computer Vision:YOLO Family Models: Get a thorough grounding in real-time object detection, an essential element of Computer Vision, by working with models from the YOLO family.ResNets: Dive deep into ResNets, a type of Convolutional Neural Network (CNN) specifically designed to solve issues like vanishing and exploding gradients in deep neural networks. You'll understand why these are game-changers in image recognition tasks.Image Classification: Explore the exciting world of image classification, where you'll learn to categorize images based on their content.Advanced Classification Project: Put your newly acquired skills to the test in a comprehensive project that involves classifying whether a person has Diabetes or not based on the data.This course is packed with the latest trends and techniques in Computer Vision, helping you stay ahead of the curve in this rapidly evolving field. You'll also learn the underlying theory behind these advanced technologies, which will enable you to adapt and thrive as new models and methods emerge.Enroll now, and start your journey towards becoming a confident Computer Vision practitioner, capable of handling any challenge thrown your way. This is more than just a course — it's your gateway into the cutting-edge world of Computer Vision. Join us and step into the future!________________________________________________________________________________________________Computer Vision, YOLO, ResNet, Image Classification, CNN, Deep Learning, Data Science, Machine Learning, AI for Beginners, Object Detection, Image Recognition, Deep Neural Networks, Udemy Courses, Online Learning, Educational Tech, Skill Development.

Who this course is for
Absolute Beginners in Machine Learning and Computer Vision
Intermediate Students with very basic knowledge of Python and Computer Vision

https://rapidgator.net/file/9c8092a77c3d9e1e30ff3fe570de8008/Learn_Computer_Vision_With_Four_End-End_Projects.part1.rar.html
https://rapidgator.net/file/8232f2ac773d88eeb9015bec74782fd1/Learn_Computer_Vision_With_Four_End-End_Projects.part2.rar.html

https://uploadgig.com/file/download/A088b1ad939f8b5b/Learn%20Computer%20Vision%20With%20Four%20End-End%20Projects.part1.rar
https://uploadgig.com/file/download/26799F8db53345e8/Learn%20Computer%20Vision%20With%20Four%20End-End%20Projects.part2.rar

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