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Tzu-Heng's wiki
Machine Learning
Traditionals
Deep Learning
Image Classification (CNN)
Detection
Semantic Segmentation
Generative Adversarial Networks
Style Transfer
Recommender Systems
Meta Learning
Notes
Differientiable Sampling and Argmax
GAN theory
Multi-task Learning (MTL)
Disentanglement in GANs
CNN practical notes
3D Clothes
OpenGL
Generative Art
nginx usage
Deploy Deep Learning Models
Character Motion Synthesis
Data Structure & Algorithms
Sorting Algorithms
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GitBook
Traditionals
some traditional machine learning algorithms
Survey Papers / Repos
Top 10 algorithms in data mining.
[ICDM'06]
josephmisiti/awesome-machine-learning
Resources
Coursera Machine Learning
by Andrew Ng
Tasks
Supervised
Linear Regression
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y=ax+b\\ L(y,\hat{y}) = (y-\hat{y})^2
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Logistic Regression
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y=\frac{1}{1+e^{-(ax+b)}} \\ L(y,\hat{y}) = -\hat{y}\log y - (1 - \hat{y}) \log (1-y)
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Naive Bayes
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P(A|B) = \frac{P(B|A)P(A)}{P(B)}
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Support Vector Machine (SVM)
Training process: Lagrange -> Dual Problem -> SMO
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\min \frac{1}{2} ||w||^2 \\ \text{s.t.}~y^{(i)}(w^{T}x^{(i)}+b) \geq 1, i=1,...,m
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K Nearest Neighbor (kNN)
Expectation-Maximization (EM)
Linear Discrimant Analysis (LDA)
Decision Tree
Random Forest
Gradient Boosting Tree (GBDT)
Semi-supervised
Weakly-supervised
Unsupervised
Clustering
K-means
Mean-shift
DBSCAN
Principal Component Analysis (PCA)
Latent Dirichlet allocation (LDA) Topic Modeling
Others
Ensemble
K-Fold Cross Validation
Bagging
Boosting
Metrics
Text
True Samples
False Samples
Predict True
True Positive
False Positive [Type I Error]
Predict False
False Negative [Type II Error]
True Negative
Precision and Recall
Precision
=
TP
TP
+
FP
\text{Precision} = \frac{\text{TP}}{\text{TP} +\text{FP}}
Precision
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TP
+
FP
TP
Recall
=
TP
TP
+
FN
\text{Recall} = \frac{\text{TP}}{\text{TP}+\text{FN}}
Recall
=
TP
+
FN
TP
F1 Score
F1 score
=
2
⋅
Precision
⋅
Recall
Precision
+
Recall
\text{F1 score} = 2 \cdot\frac{\text{Precision} \cdot \text{Recall}}{\text{Precision} +\text{Recall}}
F1 score
=
2
⋅
Precision
+
Recall
Precision
⋅
Recall
Receiver Operating Characteristic (ROC)
TPR
=
TP
TP
+
FN
\text{TPR} = \frac{\text{TP}}{\text{TP}+\text{FN}}
TPR
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TP
+
FN
TP
FPR
=
FP
FP
+
TN
\text{FPR} = \frac{\text{FP}}{\text{FP}+\text{TN}}
FPR
=
FP
+
TN
FP
Area Under ROC (AUC)
Confusion Matrix
Reference
SVM:
https://www.cnblogs.com/jerrylead/archive/2011/03/13/1982639.html
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Survey Papers / Repos
Resources
Tasks
Supervised
Semi-supervised
Weakly-supervised
Unsupervised
Others
Ensemble
Metrics
Reference