ML

1.SVM에서 Kernel이란?

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2.Using an SVM

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3.[머신러닝]차원축소(PCA,t-SNE)

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4.[머신러닝]이상치 탐지(Anomaly Detection)

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5.[ML]Collaborative Filtering(추천시스템)

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6.[ML]Gaussian Mixture Model

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7.[ML]Gradient Descent 의 세 종류(Batch, Stochastic, Mini-Batch)

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8.[ML]Overfitting and Regularization

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9.[ML]Batch Normalization

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10.[tensorflow]tf.data.Dataset 클래스 파헤치기

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11.[Tensorflow] 오디오 데이터 전처리하기1(librosa, fft, log- melspectrogram)

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12.[ML]머신러닝 평가지표 뜯어보기(confusion matrix,P-R curve, ROC-AUC..)

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13.[ML]cosine similarity VS euclidean distance (+ KL divergence)

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14.[머신러닝]모델 평가 방법(Holdout, k-fold validation, bootstrap)

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15.[ML]Scikit-learn VS Tensorflow

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16.[ML]Logistic Regression(로지스틱 회귀), Softmax function, Cross entropy 정리

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17.[ML]PCA(Principal Component Analysis)

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18.[딥러닝]python으로 CNN 컨볼루션 구현하기

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19.[ML]최적화 알고리즘 2가지(optimization algorithm)

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20.[ML]내가 생각하는 머신러닝의 정의

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21.[딥러닝]Vanshing Gradient Problem과 해결하는 여러가지 방법(BN, init, ReLU, Residual Network)

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