/Deep-Learning-Study

Deep Learning Study : Deep Learning Technical Interview Study.

Deep-Learning Study

Deep-Learning Technical Interview Study

Deep-Learning 기술 면접 스터디


👋 member

Heo Jeong Eun Cheon Jun Seok Kim Min Ji
Jen Jun MJ

🚀 goal

  • Learn Deep Learning, Computer Vision, Pytorch
  • Preparing for a Technical Interview
  • Participation in the Kaggle
  • DL과 CV, Pytorch를 학습해 기술 면접을 준비하고, Kaggle 대회 참가를 목표로 합니다.

🫡 rule

schedule && progress

  • 주말까지 정해진 학습 범위를 공부합니다.
  • 매주 주말 온 / 오프라인 스터디를 통해 일정 조율 및 학습 내용 복습과 질의응답 시간을 갖고 Kaggle 예제 풀이를 합니다.
  • 코드나 파일은 Commit Rule에 맞게 '개인' Branch PR을 올립니다. (main Branch로 Merge X, Assignees에 본인 Tag)
Date Content Study(On / Offline) Assignment
2023-12-17 CS231n : Lecture 1. Introduction to Convolutional Neural Networks for Visual Recognition
Pytorch Tutorial : 파이토치(PyTorch) 시작하기		 Lecture 1. Review -
2023-12-23 CS231n : Lecture 2. Image Classification Pipeline
Pytorch Tutorial : Introduction to PyTorch on YouTube		 Lecture 2. Review -
2024-01-07 CS231n : Lecture 3. Loss Functions and Optimization Lecture 3. Review -
2024-01-20 CS231n : Lecture 4. Back Propagation and Neural Networks Lecture 4. Review -
2024-02-04 CS231n : Lecture 5. Convolutional Neural Networks Lecture 5. Review -
2024-02-10 CS231n : Lecture 6. Training Neural Networks 1 Lecture 6. Review -
2024-02-18 CS231n : Lecture 7. Training Neural Networks 2 Lecture 7. Review -
2024-03-02 CS231n : Lecture 8. Deep Learning Software
CS231n : Lecture 9. CNN Archtiectures		 Lecture 8-9. Review -
2024-03-09 CS231n : Lecture 10. Recurrent Neural Networks Lecture 10. Review -

commit

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 Body : Information

# ADD : 파일 추가
# DOCS : 문서 수정

ex) [ADD] CS231n Lecture 1.

📚 reference