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스터디 자료와 관련해서 어떤 토의나 의견 모두 감사합니다! Github Discussion에 글을 남겨주셔도 좋고 각 포스팅 하단에 있는 Giscus 댓글창에 코멘트들을 남겨주시면 됩니다.

Welcome to any comments or opinions on our content! You can leave messages using the direct Discussion or the Giscus window on each post.

@Gueltto9th

Group Members

MIT 6.5940 Fall 2023 TinyML and Efficient Deep Learning Computing

강의 사이트: https://hanlab.mit.edu/courses/2023-fall-65940

  • 목표: 강의 영상 리뷰 및 Lab 실습 완료
  • Github 정리 자료 Archiving (참고: GNN Study)

참고강의:

21Lec + 4Lab

  • Lec1/2와 Lab0은 제외
  • 강의를 듣고 1명씩 돌아가면서 강의 복습 recap 발표
  • 다른 사람들은 질문/디스커션 토픽 가져오기
  • 주 1회 (약 16주 - 4개월 이내 완료 목표)

[Chapter I: Efficient Inference]

  • Lecture 3: Pruning and Sparsity (Part I) @ooshyun
  • Lecture 4: Pruning and Sparsity (Part II) @curieuxjy
  • Lab 1 @CastleFlag
  • Lecture 5: Quantization (Part I) @ooshyun
  • Lecture 6: Quantization (Part II) @curieuxjy
  • Lab 2 @CastleFlag
  • Lecture 7: Neural Architecture Search (Part I) @ooshyun
  • Lecture 8: Neural Architecture Search (Part II) @curieuxjy
  • Lab 3 @CastleFlag
  • Lecture 9: Knowledge Distillation @ooshyun
  • Lecture 10: MCUNet: TinyML on Microcontrollers @curieuxjy
  • Lecture 11: TinyEngine and Parallel Processing @CastleFlag

[Chapter II: Domain-Specific Optimization]

  • Lecture 12: Transformer and LLM (Part I) @ooshyun
  • Lecture 13: Transformer and LLM (Part II) @curieuxjy
  • Lecture 14: Vision Transformer @CastleFlag
  • Lab 4 @ooshyun
  • Lecture 15: GAN, Video, and Point Cloud @curieuxjy
  • Lecture 16: Diffusion Model @CastleFlag
  • Lecture 17: Distributed Training (Part I) @ooshyun
  • Lecture 18: Distributed Training (Part II) @curieuxjy
  • Lab 5 @CastleFlag
  • Lecture 19: On-Device Training and Transfer Learning @ooshyun
  • Lecture 20: Efficient Fine-tuning and Prompt Engineering @curieuxjy

[Chapter IV: Advanced Topics]

  • Lecture 21: Basics of Quantum Computing @CastleFlag
  • Lecture 22: Quantum Machine Learning @ooshyun
  • Lecture 23: Noise Robust Quantum ML @curieuxjy