Intro_to_ML_Lecture_Note
No description available.
파일 탐색기
최종 버전 다운로드 (.zip)- Autograd_Introduction.ipynb
- Bayesian_Linear_Regression_MCMC.ipynb
- KNN1.ipynb
- Logistic_Regression_1.ipynb
- Logistic_Regression_2.ipynb
- MNIST_Classification.ipynb
- NMF.ipynb
- NMF_Newsgroup20.ipynb
- PCA_MNIST.ipynb
- PCA_Newsgroup20.ipynb
- Perceptron.ipynb
- RBFN.ipynb
- SVM.ipynb
- SVM_vs_LogReg.ipynb
- Weight_Analyzer_Multiclass.ipynb
- adaptive_basis1.png
- adaptive_basis2.png
- bayes_linreg_mlp.pdf
- bayes_logreg.pdf
- conditionals.pdf
- dummy
- gamma.pdf
- gauss.pdf
- joint.pdf
- kmeans_exp.png
- kmeans_local.png
- kmeans_local2.png
- kmeans_max.png
- knn_1.png
- knn_100.png
- knn_20.png
- knn_5.png
- loss.pdf
- mds_cities.png
- neverever.png
- nmf.png
- nmf_doc.png
- overfit1.pdf
- overfit2_tes.pdf
- overfit2_tra.pdf
- perceptron_failure.pdf
- xor_transform.pdf
- curve.png
- hw1.pdf
- hw1.tex
- hw2-Copy1.ipynb
- hw2.ipynb
- hw2.pdf
- hw2.tex
- hw3.ipynb
- hw3.pdf
- hw3.tex
- hw4.ipynb
- hw4.pdf
- hw4.tex
- hw5.pdf
- hw5.tex
- hw6.pdf
- hw6.tex
- hw6_nmf.ipynb
- hw6_pca.ipynb
- mnist.npz
- mysterious_image_t.p
- 01 - Perceptron - Linear Separability.ipynb
- 02 - Logistic Regression.ipynb
- 03 - SVM vs LogReg.ipynb
- 04 - Overfitting, Regularization, and Model Selection.ipynb
- 05 - Multiclass Classification.ipynb
- 06 - Cross Validation.ipynb
- 07 - KNN.ipynb
- 08 - RBFN.ipynb
- 09 - ARBFN1.ipynb
- 10 - ARBFN2.ipynb
- 11 - Distributions Recap.ipynb
- 12 - Linear Regression.ipynb
- 13 - PCA_Face.ipynb
- 13 - PCA_News.ipynb
- 14 - NMF_Face.ipynb
- 14 - NMF_News.ipynb
- 15 - KMeans.ipynb
- 16 - MDS.ipynb
- airports-extended.dat
- AmesHousingFiltered.xls
- DataDocumentation.txt
- routes.dat
- Adaptive Basis Function Network 1.ipynb
- airports-extended.dat
- airports.dat
- Autograd_Introduction.ipynb
- Bayesian Logistic Regression.ipynb
- Bayesian_Linear_Regression.ipynb
- Bayesian_Linear_Regression_MCMC.ipynb
- Distributions.ipynb
- Gaussian_Process.ipynb
- HW2.ipynb
- HW3.ipynb
- kmeans-basic.ipynb
- KNN 1.ipynb
- KNN1.ipynb
- Linear Regression 1.ipynb
- Linear Regression 2.ipynb
- Linear Regression Adaptive Basis Function Network Bayesian.ipynb
- Linear Regression Adaptive Basis Function Network.ipynb
- Logistic Regression 1.ipynb
- Logistic Regression 2.ipynb
- Logistic Regression Weight Decay.ipynb
- Loss Functions.ipynb
- MDS.ipynb
- MNIST Classification.ipynb
- NMF.ipynb
- NMF_Newsgroup20.ipynb
- PCA_MNIST.ipynb
- PCA_Newsgroup20.ipynb
- Perceptron1.ipynb
- Perceptron2 - Linear Separability 2.ipynb
- RBFN 1.ipynb
- RBFN Early Stopping.ipynb
- RBFN SVM.ipynb
- RBFN1.ipynb
- Rotation.ipynb
- routes.dat
- SVD-airports.ipynb
- SVD_MNIST.ipynb
- SVM 1.ipynb
- SVM vs LogReg.ipynb
- SVM Weight Decay.ipynb
- Weight Analyzer.ipynb
- Weight_Analyzer_Multiclass.ipynb
- lecture_note.bib
- lecture_note.pdf
- lecture_note.tex
- Makefile
- README.md
# 설치 가이드
1. 코드 내려받기
git clone https://github.com/nyu-dl/Intro_to_ML_Lecture_Note
깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.
cd Intro_to_ML_Lecture_Note
방금 내려받은 프로젝트 폴더 안으로 이동합니다.
2. Make
보통 추천사전 준비물
- Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
- Make Linux/macOS는 보통 기본 설치되어 있습니다. Windows는 별도 설치(예: MSYS2, WSL)가 필요합니다.
make
생성된 빌드 설정을 바탕으로 실제 컴파일을 진행해 실행 파일을 만듭니다.
에러 없이 끝나면 성공입니다. 생성된 실행 파일을 직접 실행해보세요.
// repository documentation
Was this content helpful?
(0 ratings)