[파이토치] 실전 인공지능으로 이어지는 딥러닝 - 기초부터 논문 구현까지
딥러닝호형
인공지능 분야에서 활용도가 매우 높은 딥러닝 프레임워크인 Pytorch를 이용하여 다양한 인공 신경망을 구현하는 강의입니다.
초급
딥러닝, Python, PyTorch
Learn how to understand advanced machine learning models without math and implement them easily in Python.
102 learners

Overall flow of machine learning work
How to use machine learning basics to advanced models easily
Building machine learning models using Python
Even if it's your first time, it's OK even if you don't know much about math!
Get started building Python ML models.
Even if you're not familiar with math , this course is for those new to machine learning, focusing on quickly and efficiently learning everything from data preprocessing to advanced machine learning techniques.
Rather than focusing on formulas, the lecture focuses on data preprocessing techniques and the concepts, strengths, and weaknesses of each machine learning model. The content is structured so that students can immediately apply it through hands-on practice . Furthermore, this single lecture will allow you to understand the entire machine learning workflow .
We've created this course to provide you with the essential machine learning knowledge you need for competitive competitions and practical applications. Let's take on the challenge together!
💡 Through lectures , you'll gain an understanding of machine learning and engage in a variety of practical exercises based on what you've learned . The content also includes practical experience gained through practical work .
💡 It handles real-world data such as NASA airfoil noise data and credit rating data, and can learn advanced machine learning such as ensemble/autoML quickly and efficiently.
💡 Solid foundation from basics to practical application! We provide 110 pages of extensive learning materials and 19 practice files, including basic Python grammar and machine learning examples. If you have any questions during class, please leave a question.
Nice to meet you, I'm Deep Learning Hohyung!
I'm Deep Learning Hohyung, currently running a YouTube channel dedicated to deep learning and machine learning. Drawing on my background in data analysis and mathematics, as well as my practical experience, I provide essential information. To date, approximately 3,000 students have chosen to take my courses.
Q. Can non-majors also take the course?
Anyone interested in getting started with machine learning can enroll! Furthermore, we've kept the math content to a minimum, keeping in line with the course's objectives.
Q. Is programming knowledge required?
Basic Python concepts are also covered in the course, so it is not required.
Q. Why should I take this course?
The course is structured based on specialized knowledge and diverse project experience, covering the entire machine learning process. This will help you develop a comprehensive understanding of machine learning tasks . Additionally, it will help you write code more efficiently.
Q. Is mathematical knowledge required?
A basic understanding of functions is all you need. Those who wish to develop machine learning models themselves or conduct optimization research will need to study additional mathematics beyond this course.
Q. What program do you use?
All exercises are conducted on Google Colaboratory, which requires no separate installation. A free Google account is required, and failure to use Colaboratory may result in disruption to the exercises.
Who is this course right for?
Anyone interested in machine learning/data analysis
Those who want to acquire essential knowledge in machine learning/data analysis
Need to know before starting?
Passion to do
4,897
Learners
342
Reviews
261
Answers
4.7
Rating
7
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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)
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34 lectures ∙ (4hr 31min)
Course Materials:
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$51.70
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