최신 딥러닝 기술 Vision Transformer 개념부터 Pytorch 구현까지
딥러닝호형
딥러닝 최신 기술 중 하나인 Vision Transformer를 공부하고 Pytorch를 이용하여 논문을 구현하는 강의입니다. 비전 분야의 새로운 미래를 저와 함께 경험해 봐요!
Intermediate
Vision Transformer, 딥러닝, PyTorch
BEST
Linear Algebra Leading to Machine Learning/Deep Learning
Covers linear algebra, which is essential for machine learning/deep learning research.
This course is prepared for Beginners.
What you will learn!
Basic Mathematics Required for Machine Learning/Deep Learning
The relationship between linear algebra and machine learning
Essential Mathematical Expressions
Leading to machine learning/deep learning
Let's learn linear algebra by linking it with machine learning 📖
Hello, I am Deep Learning Ho-hyung, who currently runs a YouTube channel related to deep learning/machine learning .
Based on knowledge of mathematics/data analysis, experience with numerous deep learning/machine learning projects, and career as a research engineer
We will go over the contents that you absolutely must study.
Knowledge of mathematics and machine learning/deep learning
Are you still separated?
artificial neural network
Manufacturing, autonomous vehicles, healthcare, biotechnology, robotics, etc.
It is a powerful artificial intelligence technology that is being applied in a wide range of fields.
In fact, the number of paper submissions is increasing every year, and many universities around the world, including Korea, are opening AI-related departments, and the industry is making significant investments. This lecture is a mathematics lecture that allows you to study deep learning/machine learning in line with this trend.
This course covers the linear algebra required to study deep learning/machine learning/data analysis .
Have you ever thought about whether you should study “mathematics” while studying artificial intelligence or data analysis?
Or have you ever had trouble understanding machine learning algorithms because you lacked basic math skills?
As a math/data analysis major, I will tell you 'where' and 'why' math is used.
What you will learn in this course ✏️
Are you still just using libraries without understanding the algorithms?
Model optimization and tuning require mathematical understanding.
In this lecture , we will discuss the basics of linear algebra and machine learning.
I'll answer your questions in advance 🙋🏻♂️
Q. Can non-majors also take the course?
Yes, of course. You can take it regardless of your major!
Q. What are the benefits of learning linear algebra?
All data is converted into matrix form and operations are performed on it.
Therefore, if you know the linear algebra that can handle matrices, it will ultimately be easy to process data.
Q. Are there any special advantages to this course?
As a former mathematics/data analysis major, I created this lecture with the goal of conveying the concepts that must be known in an easy and compact manner, so even those who find mathematics difficult can fully listen to it. Also, rather than focusing on mathematical proofs, you can learn how mathematics is used in the actual data analysis and artificial intelligence fields, and draw the overall picture.
If you want to get started with deep learning, please refer to the lecture on grasping deep learning concepts leading to practical artificial intelligence .
Recommended Roadmap 🚩
Recommended for
these people!
Who is this course right for?
For those who are new to machine learning and deep learning
People who lack basic math skills
For those who are curious about how linear algebra is used in machine learning/deep learning
Need to know before starting?
Passion to do
Hello
This is dlbro
4,725
Students
319
Reviews
257
Answers
4.7
Rating
7
Courses
안녕하세요.
딥러닝/머신러닝 관련 유튜브를 운영하는 딥러닝 호형입니다.
수학/데이터 분석을 전공하고 다수의 딥러닝 프로젝트를 완료하고 수행하고 있습니다.
머신러닝, 고급 머신러닝, 딥러닝, 최적화 이론, 강화 학습 등의 인공지능 내용과 선형 대수학, 미적분, 확률과 통계, 해석학, 수치해석 등의 수학 내용까지 여러분들과 공유할 수 있는 지식을 가지고 있습니다.
모두 만나서 반갑습니다!
* 관련 이력
현) SCI(E) 논문, 국제 학회 발표 다수
현) 인공지능 관련 대학교 자문 다수
전) K기업 전임 연구원 - 데이터 분석 및 시뮬레이션: 신제품 개발, 성능 향상, 신기술 적용
"딥러닝을 위한 파이토치 입문" 저서 (세종도서 학술부문 2022 우수도서로 선정)
Curriculum
All
24 lectures ∙ (3hr 4min)
Course Materials:
Lecture resources
Published:
Last updated:
Reviews
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41 reviews
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