Latest deep learning technology and object recognition
This course will teach you from the early YOLO model, a real-time object recognition model, to the latest model. In addition, you will learn various deep learning techniques along with object recognition.
With the latest version, YOLOv6 Learn object recognition models and deep learning 🔥
Real-time object recognition model All versions of YOLO at once! 🗂️
Object recognition models are a popular topic, ranking among the top five keywords in computer vision. In this lecture, we'll learn about object recognition models using the latest model, YOLOv6. Object recognition models have been developed by actively utilizing the latest deep learning techniques. Therefore, while studying object recognition, you'll also gain exposure to various deep learning techniques . (All lecture materials are provided.)
This course requires a basic understanding of deep learning and is designed for those new to object recognition. The goal is to broaden your understanding of object recognition modelsanddeep learning knowledge . Therefore, while focusing on object recognition, I will also introduce a wide range of deep learning techniques.
Lecture Features ✨
1️⃣
Real-time object recognition model YOLOv1 to v6 All-in-one lecture covering
2️⃣
Object recognition, artificial neural networks, Normalization techniques, etc. Includes various deep learning technologies
3️⃣
Until 2022 42 papers and See AI Report Part 1
What is the YOLO model? 🤔
YOLO is one of the best models for real-time object recognition . The latest model, YOLOv6 (2022), boasts extremely high image processing speed and is lightweight, making it suitable for practical industrial applications .
What you'll learn 📚
Introduction to Object Recognition and Evaluation Metrics
YOLOv1
YOLOv2
YOLOv3
YOLOv4
YOLOv6
YOLOv6 Practice
How to Become an Expert (How to Read Papers)
Expected Questions Q&A 💬
Q. Can I understand object recognition models with only basic knowledge of deep learning?
This course is designed for those with basic knowledge of deep learning. While it may seem challenging, it's a valuable resource for expanding your understanding. The course's difficulty has been adjusted to maximize comprehension.
Q. Why should I study object recognition?
Object recognition models are being developed by actively leveraging the latest image processing technologies, allowing you to learn a variety of techniques simultaneously, making them a valuable field for broadening your knowledge of deep learning. Therefore, if you're interested in deep learning, this course is definitely beneficial. Furthermore, it's a highly versatile technology.
Q. What program do you use?
YOLOv6 training is conducted on Google Colaboratory, which requires no separate installation. A free Google account is required, and failure to use Colaboratory may result in problems with the training.
Why Deep Learning? ✒️
Based on my knowledge of mathematics/data analysis , experience with numerous deep learning/machine learning projects , and career as a research engineer, I will point out the content you must study.
Numerous SCI(E) papers and presentations at international conferences
Many universities advise on artificial intelligence
Author of "Introduction to PyTorch for Deep Learning" (selected as a 2022 Sejong Books Academic Excellence Book)
Recommended for these people
Who is this course right for?
Anyone who wants to learn object recognition quickly
Those who want to acquire various deep learning knowledge
Those preparing for graduate school related to artificial intelligence
Need to know before starting?
Understanding the concept of deep learning leading to practical artificial intelligence
I am Deep Learning Ho-hyung, and I run a YouTube channel related to deep learning and machine learning.
I majored in mathematics/data analysis and have completed and am currently working on numerous machine learning/deep learning projects.
I have knowledge that I can share with you, ranging from Artificial Intelligence topics such as machine learning, advanced machine learning, deep learning, optimization theory, and reinforcement learning, to mathematical content including linear algebra, calculus, probability and statistics, analysis, and numerical analysis.
Nice to meet you all!
§ Profile
Research Engineer at a large corporation - Development of smart factory-related models
PhD in Mathematics from Germany
§ Related Experience
Numerous SCI(E) papers and international conference presentations
Multiple university consultations related to artificial intelligence
Doctoral/Post Doctoral Researcher at a German Research Institute
Major Corporation Research Engineer - New Product Development
Author of "Introduction to PyTorch for Deep Learning" (Selected as a 2022 Sejong Book in the Academic Category)