CS229

all the ppts, notes and homework for CS229 taught by Andrew Ng

Name		Name	Last commit message	Last commit date
mathematics_foundation		mathematics_foundation
python_tutorial		python_tutorial
stanford-cs229		stanford-cs229
BiasVarianceAnalysis.pdf		BiasVarianceAnalysis.pdf
EM.pdf		EM.pdf
Factor analysis.pdf		Factor analysis.pdf
Generative Model.pdf		Generative Model.pdf
ICA.pdf		ICA.pdf
K-means.pdf		K-means.pdf
LearningTheory.pdf		LearningTheory.pdf
Linear Model.pdf		Linear Model.pdf
ML-advice.pdf		ML-advice.pdf
Mixtures of Gaussians.pdf		Mixtures of Gaussians.pdf
PCA.pdf		PCA.pdf
README.md		README.md
Regularization and model selection.pdf		Regularization and model selection.pdf
SVM.pdf		SVM.pdf
boosting.pdf		boosting.pdf
critiques-ml-aut19.pdf		critiques-ml-aut19.pdf
cs229-notes-backprop.pdf		cs229-notes-backprop.pdf
cs229-notes-deep_learning.pdf		cs229-notes-deep_learning.pdf
cs229-notes-dt.pdf		cs229-notes-dt.pdf
cs229-notes6.pdf		cs229-notes6.pdf
decision_tree_demo.ipynb		decision_tree_demo.ipynb
deep_learning.pdf		deep_learning.pdf
evaluation_metrics.pdf		evaluation_metrics.pdf
loss-functions.pdf		loss-functions.pdf
midterm-review.pdf		midterm-review.pdf
reinforce_learning.pdf		reinforce_learning.pdf
representer-function.pdf		representer-function.pdf
weak_supervision_notes.pdf		weak_supervision_notes.pdf
weak_supervision_slides.pdf		weak_supervision_slides.pdf

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