NoML
Introduction
ML101
Performance Metrics
Bias and variance
L1/L2
MLE/MAP/Bayesian Method
Generative Model and Discriminative Models
Preprocessing
Numeric Features
Categorical and Ordinal Features
Datetime and Coordinates
Handling Missing Values
Meta algorithms
Adaboost
Gradient Boosting Decision Tree
Meta Algorithms
Deep Learning
Basics
CNN
CNN_blocks
Classic networks
Data agumentation
Frameworks
Pandas
Andrew Ng's guideline
Tensorflow
PyTorch
Keras
Classification
Tree
C4.5/CART
RandomForests
Logistic Regression
SVM
Regression
test
Clustering
test
Code
code
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Data agumentation
Common augmentation method(CV)
mirroring, random cropping, ration, shearing, local warping
color shifting
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