AI Research Engineer를 위한 지침서

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.

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

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

Basics

• Python

• Numpy

• PyTorch

DevTools & Ops

• Git

• Docker

Frontend (Optional)

• Streamlit

• Gradio

Backend (Optional)

• FastAPI

• DB

Paper Implementation

Computer Vision

• Style Transfer

Natural Language Processing

• Sequence to Sequence

  
  

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.)