YOLO(You Only Look Once) 논문을 TensorFlow 2.0을 이용해서 밑바닥부터 구현해보며 딥러닝 논문 구현 능력을 배울 수 있는 강의입니다.
딥러닝 논문 읽는 법
딥러닝 논문 구현하는 법
YOLO 모델 구조에 대한 디테일한 이해
Object Detection 문제영역에 대한 배경지식
TensorFlow 2.0을 이용한 코드 작성법
딥러닝 연구자 필수 소양, 최신 논문 구현 능력!
YOLO 구현과 함께 익혀보세요 😀
많은 기업들에서 딥러닝 연구자를 채용할때 최신 논문을 직접 구현해본 경험을 우대하고 있습니다. YOLO(You Only Look Once) 논문을 직접 구현해보면서 최신 논문 구현 경험을 익혀보세요.
YOLO 논문을 함께 읽으며 YOLO 구조를 완벽하게 파악한 뒤✍️,
TensorFlow 2.0을 이용해서 YOLO를 직접 구현해봅시다.👨🏻💻
YOLO 논문(You Only Look Once: Unified, Real-Time Object Detection)을 같이 읽고, YOLO 모델을 TensorFlow 2.0을 이용해서 밑바닥부터 구현해봅니다. 또한 구현한 YOLO 모델을 이용한 Cat Detector를 만들어 봅니다.
👋 본 강의는 TensorFlow 2.0과 딥러닝 기초에 대한 선수지식이 필요한 강의입니다. 반드시 아래 강의를 먼저 수강하시거나 그에 준하는 지식을 갖춘 뒤 본 강의를 수강하세요.
딥러닝 핵심 이론과 최신 TensorFlow 2.0을 이용한 딥러닝 코드 구현을 한번에 배울 수 있는 강의입니다.
Q. 딥러닝 논문 구현을 경험해보면 무엇이 좋나요?
학습 대상은
누구일까요?
딥러닝 논문을 읽고 구현하는 능력을 기르고 싶은 분
딥러닝 연구 관련 직종으로 취업을 원하시는 분
인공지능/딥러닝 관련 연구를 진행하고 싶은 분
인공지능(AI) 대학원을 준비 중이신 분
선수 지식,
필요할까요?
Python 사용경험
선수강의 [TensorFlow 2.0으로 배우는 딥러닝 입문] 수강경험
8,381
명
수강생
588
개
수강평
345
개
답변
4.6
점
강의 평점
28
개
강의
전체
30개 ∙ (3시간 37분)
해당 강의에서 제공:
전체
32개
4.7
32개의 수강평
수강평 1
∙
평균 평점 3.0
3
This is not a useful lecture for beginners. Here, beginners mean people who have not written deep learning code with expert type code. At least, it would be useful if you have written deep learning code in a functional way and can partially use other people's deep learning code through git cloning. However, it would be useful if you have not previously graduated from a related department and are thinking of writing code yourself in graduate school or want to experience it, and if you are willing to look over the entire course and make additional supplements according to your career development. The quality of the lecture itself is not that high. Most of the explanations and lecture materials are not very friendly or detailed, and the handwriting is done with a mouse rather than a Wacom pen. However, since there is no other lecture that can replace it, it has the advantage of being a lecture that implements papers into code.
Hello. Thank you for taking the time to take the class~!. Thank you for the detailed course review~. I will try my best to create a more satisfactory course. Have a nice day!
수강평 7
∙
평균 평점 4.6
4
I'm listening to the roadmap. The recording itself was recorded with a very low voice. The recording quality (voice volume) is not consistent for each lecture, so it's a little uncomfortable to listen to the lecture. I hope you'll pay attention to this part next time^^
Hello~. First of all, I apologize for the inconvenience during the classㅠ. Next time I film, I will make sure to turn up the sound a bit louder. Thank you for taking the time to take the class~!. Have a nice day!
수강평 2
∙
평균 평점 5.0
5
From the perspective of using machine learning and deep learning in the field, this lecture broadened my frame of mind so that I could expand my career from being a 'developer' who uses existing well-structured models, to a 'researcher'. I was able to follow along well without missing the details of the mathematical part, and I was also able to understand the process of integrating this into actual implementation code. I hope that you will launch a lecture that goes beyond this lecture and covers representative papers such as BERT or GPT, or widely known techniques in model development.
Thank you~. We plan to open various lectures in the future, so please look forward to it~. Have a nice day!
수강평 1
∙
평균 평점 5.0
5
thank you
Hello. Thank you for taking the time to take the class~!. I will try my best to create more satisfactory lectures. Have a nice day!
수강평 1
∙
평균 평점 5.0
5
Good good good good good
Hello. Thank you for taking the time to take the class~!. I will try my best to create more satisfactory lectures. Have a nice day!
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![[Tensorflow2] Complete conquest of Python machine learning - Marathon record prediction project강의 썸네일](https://cdn.inflearn.com/public/courses/324207/course_cover/1a79c7dc-1624-4d3a-95ff-79ba1e9c4025/python_machine_learning.png?w=420)
