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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