이 다이얼이 100억개 모이면 챗지피티가 됩니다 | 퍼셉트론

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영상에서 다루는 퍼셉트론부터 GPT까지의 신경망 이론을 체계적으로 공부하기 위한 로드맵을 정리해드리겠습니다[1][2][3].

학습 테크트리 (순서대로)

1단계: 수학 기초 복습

선수과목: Linear Algebra, Probability & Statistics, Calculus
교재:

  • Mathematics for Machine Learning (Marc Peter Deisenroth 외) - 무료 PDF 온라인 제공
  • Deep Learning Book Part I (Ian Goodfellow 외) - Chapters 2-5: 선형대수, 확률론, 수치계산, 머신러닝 기초[2][4][5]

온라인 코스:

  • MIT 18.06 Linear Algebra (Gilbert Strang) - 무료 OCW[6]
  • Khan Academy - Linear Algebra, Probability (한국어 자막 제공)

2단계: 머신러닝 기초

교재:

  • Machine Learning (Tom Mitchell) - 고전 교재[7]
  • Pattern Recognition and Machine Learning (Christopher Bishop)

온라인 코스:

  • Andrew Ng - Machine Learning Specialization (Coursera) - 한국어 자막
  • Stanford CS229: Machine Learning (무료 강의노트)

3단계: 신경망 & 딥러닝 핵심

교재 (우선순위 순):

  1. Understanding Deep Learning (Simon Prince, 2023) - 가장 최신이고 실용적[3][8][9]

    • Transformers, Diffusion Models 등 최신 토픽 포함
    • 수학과 직관의 균형이 좋음
    • 무료 PDF + Python 실습 제공

  2. Neural Networks and Deep Learning (Michael Nielsen) - 무료 온라인[10][11]

    • 입문자 친화적
    • XOR 문제, Backpropagation 등 영상 내용 상세 설명
    • 손글씨 숫자 인식으로 실습

  3. Deep Learning (Goodfellow, Bengio, Courville) - 딥러닝 바이블[2][12][5]

    • Part I: 수학 기초 (선형대수, 확률, 최적화)
    • Part II: 실무용 딥러닝 (CNN, RNN, 정규화, 최적화 알고리즘)
    • Part III: 연구 주제 (Autoencoders, GAN, 생성 모델)

온라인 코스 (영어):

  1. Stanford CS230: Deep Learning (Andrew Ng, Kian Katanforoosh) - 2025년 강의 업데이트됨[1][13][14]

  2. Stanford CS231n: CNN for Visual Recognition[15][16][17][18]

    • Computer Vision 중심
    • 3개 프로그래밍 과제
    • 2025년 봄 강의 업데이트됨
    • 무료 강의노트: http://cs231n.github.io

  3. MIT 6.S191: Introduction to Deep Learning - 무료 강의[6]

온라인 코스 (한국어):

  1. 카이스트 경영대학원 딥러닝 강의 (YouTube)[19][20]

    • 퍼셉트론, 역전파, CNN, RNN 등
    • TensorFlow 실습 포함
    • 한국어로 설명

  2. 딥러닝 강의 6시간 완성 (메타코드M, 카이스트 AI 박사)[21]

    • Computer Vision 중심
    • 한국어 강의

  3. K-MOOC 비전공자를 위한 AI 딥러닝[22]

    • 다층퍼셉트론, 역전파, CNN, RNN
    • 한국어 강의

4단계: 고급 주제 & 최신 아키텍처

교재:

  • Attention is All You Need (Transformer 논문)
  • Understanding Deep Learning Chapters 12-21 (Transformers, Graph Networks, Diffusion Models)[3]
  • Grokking Deep Learning (Andrew Trask) - 라이브러리 없이 구현[23][24]

온라인 코스:

  • Stanford CS224n: NLP with Deep Learning
  • Fast.ai Practical Deep Learning for Coders
  • Hugging Face NLP Course (무료)

5단계: 실습 프로젝트

추천 리소스:

  • Neural Networks from Scratch in Python (nnfs.io)[23]
  • Kaggle 컴피티션
  • Papers with Code (최신 논문 + 구현)

추천 학습 순서 (CS 배경 있는 경우)

6개월 플랜:

  1. 1-2주: 수학 복습 (선형대수, 미적분 위주)
  2. 3-4주: ML 기초 (Andrew Ng 강의 또는 Bishop 책)
  3. 8-10주: DL 핵심 (Understanding Deep Learning 또는 Goodfellow 책 + CS230)[1][3]
  4. 4-6주: CNN/RNN (CS231n + 실습)[16][17]
  5. 4-6주: Transformers & LLM (Understanding Deep Learning Ch 12 + 논문)[3]
  6. 나머지: 프로젝트 & 논문 구현

핵심 과목 순서:

  1. Linear Algebra & Optimization
  2. Machine Learning Fundamentals
  3. Neural Networks & Backpropagation (영상의 퍼셉트론 내용)
  4. Deep Feedforward Networks
  5. Regularization & Optimization Algorithms
  6. Convolutional Neural Networks
  7. Recurrent Neural Networks & Sequence Modeling
  8. Attention Mechanisms & Transformers
  9. Large Language Models (GPT 아키텍처)
  10. Advanced Topics (Diffusion Models, RL 등)

소프트웨어 엔지니어 배경이 있으시니 구현 중심으로 공부하시면 빠르게 습득하실 수 있을 것입니다[1][23][2].

Sources
[1] CS230 Deep Learning - Stanford University https://cs230.stanford.edu
[2] Deep Learning Book https://www.deeplearningbook.org
[3] Understanding Deep Learning by Simon J.D. Prince | Goodreads Understanding Deep Learning by Simon J.D. Prince | Goodreads
[4] janishar/mit-deep-learning-book-pdf - GitHub GitHub - janishar/mit-deep-learning-book-pdf: MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville
[5] Deep Learning - Ian Goodfellow, Yoshua Bengio, Aaron Courville Deep Learning - Ian Goodfellow, Yoshua Bengio, Aaron Courville - Google 도서
[6] MIT OpenCourseWare | Free Online Course Materials https://ocw.mit.edu
[7] [PDF] Mitchell. “Machine Learning.” - CMU School of Computer Science https://www.cs.cmu.edu/~tom/files/MachineLearningTomMitchell.pdf
[8] Understanding Deep Learning Understanding Deep Learning
[9] [PDF] Understanding Deep Learning - Anthology of Data Science https://anthology-of-data.science/resources/prince2023udl.pdf
[10] 5 Free Resources for Understanding Neural Networks 5 Free Resources for Understanding Neural Networks - MachineLearningMastery.com
[11] Neural networks and deep learning http://neuralnetworksanddeeplearning.com
[12] A Guide to “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and … https://www.reddit.com/r/learnmachinelearning/comments/1o99uwe/a_guide_to_deep_learning_by_ian_goodfellow_yoshua/
[13] Stanford CS230 - Lecture 1: Introduction to Deep Learning - YouTube https://www.youtube.com/watch?v=_NLHFoVNlbg
[14] Stanford CS230 | Autumn 2025 | Lecture 3: Full Cycle of a DL project https://www.youtube.com/watch?v=MGqQuQEUXhk
[15] Stanford CS231n: CNN for Visual Recognition - csdiy.wiki Stanford CS231n: CNN for Visual Recognition - csdiy.wiki
[16] CS231n Deep Learning for Computer Vision https://cs231n.github.io
[17] Stanford University CS231n: Deep Learning for Computer Vision https://cs231n.stanford.edu
[18] Stanford CS231N Deep Learning for Computer Vision I 2025 https://www.youtube.com/playlist?list=PLoROMvodv4rOmsNzYBMe0gJY2XS8AQg16
[19] 카이스트 경영대학원 딥러닝 강의 - YouTube https://www.youtube.com/playlist?list=PLfbC0A7KFwZWxd6EyulzFzdLmbBvyVQpT
[20] [카이스트 경영대학원 딥러닝 강의] 네이버 하정우 소장 특강 - YouTube https://www.youtube.com/watch?v=MFFlX3jcjls
[21] 딥러닝 강의 1편 6시간 완성 - [Top AI대학원 박사] - YouTube https://www.youtube.com/watch?v=Adi0Iasehj8
[22] 비전공자를 위한 AI 딥러닝(Deep Learning) | K-MOOC 비전공자를 위한 AI 딥러닝(Deep Learning) | K-MOOC
[23] Neural Networks from Scratch in Python Book https://nnfs.io
[24] Top 11 Deep Learning Books to Read in 2025 - DataCamp https://www.datacamp.com/blog/top-10-deep-learning-books-to-read-in-2022
[25] 이 다이얼이 100억개 모이면 챗지피티가 됩니다 | 퍼셉트론 https://www.youtube.com/watch?v=KKAkNv4JrTw
[26] Professional Certificate Program in Machine Learning & Artificial … MIT | Professional Certificate Program in Machine Learning & Artificial Intelligence
[27] Stanford University Online Courses - Coursera Stanford University Online Courses | Coursera
[28] TUTORIALS - iAI KAIST - 카이스트 iAI KAIST - TUTORIALS
[29] Hi, what are the advanced courses/books in machine learning and … https://www.reddit.com/r/learnmachinelearning/comments/zy1ga7/hi_what_are_the_advanced_coursesbooks_in_machine/
[30] 교육 - KAIST 김재철AI대학원 https://gsai.kaist.ac.kr/academics/?lang=ko
[31] Neural Networks Textbooks in eTextbook Format - VitalSource Neural Networks Textbooks in eTextbook Format | VitalSource
[32] [#2.Lec] ML Basic - 딥러닝 홀로서기 (Eng Sub) - YouTube https://www.youtube.com/watch?v=hPXeVHdIdmw
[33] Deep Learning by Ian Goodfellow - Goodreads https://www.goodreads.com/book/show/24072897-deep-learning
[34] Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron … Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville [book] – BERD – Biostatistics Epidemiology and Research Design
[35] Understanding Deep Learning - Prince, Simon J.D.: 9780262048644 https://www.abebooks.com/9780262048644/Understanding-Deep-Learning-Prince-Simon-0262048647/plp
[36] Deep Learning Ian Goodfellow - eBay Deep Learning Ian Goodfellow | eBay
[37] [P] New book: Understanding Deep Learning : r/MachineLearning https://www.reddit.com/r/MachineLearning/comments/wfzxzf/p_new_book_understanding_deep_learning/
[38] Stanford CS231N Deep Learning for Computer Vision | Spring 2025 https://www.youtube.com/watch?v=2fq9wYslV0A
[39] Understanding Deep Learning - Anthology of Data Science Understanding Deep Learning – Anthology of Data Science
[40] CS 231N: Convolutional Neural Networks for Visual Recognition Loading…
[41] Understanding Deep Learning – Book Reading Kickoff Ch1+2 https://www.youtube.com/watch?v=vgI25Ykcc1Y

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