title: ML Notes
ML Notes
Typical training parameters for different problem types
Problem type
Loss function
Metrics
Final Activation Function
Binary classification
binary_crossentropy
accuracy
sigmoid
Multiclass classification
categorical_crossentropy
accuracy
softmax
Scalar regression
mse
mae
None
Links
Andrew Ng's Stanford ML Course on YouTube
https://course.fast.ai/
Options for cloud computing setup for ML. Parent site has lots of other great resources.
https://explained.ai/
Tutorials on some fundamental ML/DL topics, such as this inroduction to
Matrix math for DL
.
http://tensorlab.cms.caltech.edu/users/anima/cms165-2020.html
Caltech course with links to talks on YouTube and slides