로드맵 썸네일

AI Research Engineer를 위한 지침서

작성자 프로필 이미지

화이트박스

Python
PyTorch
컴퓨터 비전

입문 대상

로드맵 참여중인 유저 프로필 사진
로드맵 참여중인 유저 프로필 사진

5명 참여중

로드맵 코스

아직 미완성입니다

지속적으로 업데이트 예정입니다

AI Research Engineer를 위한 로드맵 입니다. 국내/해외 유수 대학 커리큘럼을 참고하여 제작 중이며 제가 직접 공부 해 보면서 꼭 필요하다고 느껴지는 basics를 포함 시켰습니다.

THEORY (Basics)

1. Linear Algebra

  1. Vector

  2. Linear combinations, Span, Basis vectors

  3. Linear transformations and Matrices (including 3D)

  4. Matrix multiplication as composition

  5. Determinant

  6. Inverse matrices, Column space, Null space

  7. Nonsquare matrices as transformations

  8. Dot products

  9. Cross products

  10. Change of basis

  11. Eigenvectors and Eigenvalues

2. Probability and Statistics

  1. Populations and Samples

  2. Inference

  3. Law of Large Numbers

  4. Central limit theorem (Generating random numbers)

  5. Multivariate Statistics

  6. What is Probability (including conditional probability)

  7. Random Variable

  8. Random Vectors

  9. Bayes Rule

  10. Linear Transformation of Random Variables

THEORY (ML/DL)

3. Machine Learning

  1. What is Machine Learning

  2. Optimization

  3. Linear Regression

    1. single variable, multi variables

    2. overfitting, regularization(LASSO), L1 norm, L2 norm

  4. Classification

    1. KNN

    2. Perceptron

  5. SVM

  6. Logistic Regression

  7. Maximum Likelihood Estimation (i.e. MLE)

  8. Clustering: K-means

  9. PCA

4. Deep Learning

  1. Difference between ML and DL

  2. Neural Network

  3. Discriminative AI

  4. Generative AI

  5. Autoencoder

  6. Convolutional Neural Network

  7. Recurrent Neural Network

  8. Attention

  9. Transformer

ENGINEERING

Basics

  • Python

  • Numpy

  • PyTorch

DevTools & Ops

  • Git

  • Docker

Frontend (Optional)

  • Streamlit

  • Gradio

Backend (Optional)

  • FastAPI

  • DB

PROJECT

Paper Implementation

Computer Vision

  • Style Transfer

Natural Language Processing

  • Sequence to Sequence


FURTHER

특정 Industry의 문제를 풀거나 (e.g. Finance, Bio technology, etc.)

특정 Domain을 더 깊게 공부 하거나 (e.g. Computer Vision, NLP, etc.)

특정 Research Topic을 깊게 파고들거나 (e.g. Generative AI, Object Detection, Machine Translation, etc.)

로드맵 상세보기

3개 코스

로드맵에 포함된 외부링크 썸네일
YouTube
Vectors | Chapter 1, Essence of linear algebra
Beginning the linear algebra series with the basics.Help fund future projects: https://www.patreon.com/3blue1brownAn equally valuable form of support is to s...

로드맵 코스 3