Page Index - leemik3/tensorflow-2.0 GitHub Wiki

196 page(s) in this GitHub Wiki:

• Home
• Deep Learning
• Index
• ~ing
• CNN
• Time Series Analysis
• Attention Mechanism
• Optimization
• Manifold Learning
• Transfer Learning
• Meta Learning
• Clustering
• Generative Model
• Normalization
• Statistic
• (~ing) Conv1D, Conv2D, Conv3D 데이터 형태 정리
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• (~ing) height, width, channel, length, depth 관계 정리
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• (~ing) Invariance
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• (~ing)Saturation Problem
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• 2D Image Filtering
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• Accuracy, Precision, Recall, F1 score
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• Activation Function
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• Anomaly Detection
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• AR, MA, ARMA, ARIMA
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• Attention Mechanism
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• Autoencoder
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• Batch Normalization
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• Batch, Iteration, Epoch
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• Bayes' Theorem
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• Bidirectional RNN
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• Categorical Variable Encoding
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• Class Imbalance
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• Clustering
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• CNN
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• CNN Algorithms
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• Coarse grained, Fine grained
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• Context Awareness
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• Conv1D
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• Covariance Matrix (Dispersion Matrix)
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• CPU와 GPU
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• Dataset Shift
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• Deep Learning Algorithm
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• Depthwise Saparable Convolution
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• Dimensionality Reduction
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• Distributed Learning
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• Ensemble
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• FLOPS
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• Fuzzy
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• Gaussian Filter VS Bilateral Filter
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• Gaussian Noise Removal
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• Generative Model
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• Genetic Algorithm (GA)
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• Grid Search
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• Ground truth
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• GRU
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• High Level Features
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• homogeneous
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• iid
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• Image Interpolation
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• Index
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• inter intra class
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• Joint Training, Alternate Training
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• Local Contrast Normalization
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• Local Receptive Field
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• Local Response Normalization
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• Log Linear Model
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• Long Term Dependency
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• Loss Function
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• LSTM
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• Machine Learning 회귀 모델의 평가 지표
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• Manifold Learning
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• Maximum Likelihood
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• Meta Learning
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• ML DL 학습 알고리즘 분류
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• Multi Layer Perceptron (MLP)
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• Multiheaded architecture
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• Optimization
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• Prior & Posterior Distribution
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• RNN
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• RNN 계층과 셀
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• RNN의 역전파 : BPTT
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• RNN의 유형
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• Sequential Data
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• Sliding Window
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• Sparse Coding
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• Sparse Connection
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• Spatiotemporal
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• Statistical Machine Translation
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• Time Series Analysis
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• Transfer Learning
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• Unit, input_shape 정리
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• Unrolled LSTM
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• Vanishing Gradient Problem
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• Variational Autoencoder
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• Weight Sharing
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• 가중치 초기화 방법
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• 결측값 처리 방법
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• 계층적 시계열, 그룹화 시계열
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• 딥러닝의 문제점과 해결 방안
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• 시계열 데이터 유형
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• 시계열 형태
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• 이런저런 layer들 정리,,
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• 일부 내용
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• 자료 유형
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• 정규화, 규제화, 표준화
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