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ML - COURSE - Coggle Diagram
ML - COURSE
CONCEPTS
FRAMING
DESCENDING INTO ML
REDUCING LOSS
TENSORFLOW (1st steps)
GENERALIZATION
TRAINING AND TEST SETS
VALIDATION SET
REPRESETATION
FEATURE CROSSES
REGULARIZATION: SIMPLICITY
LOGISTIC REGRESSION
CLASSIFICATION
REGULARIZATION: SPARCITY
NEURAL NETWORKS
TRAINING NEURAL NETWORKS
MULTI-CLASS NEURAL NETWORKS
EMBEDDINGS
STATIC vs DYNAMIC TRAINING
STATIC vs DYNAMIC INFERENCE
DATA DEPENDENCIES
FAIRNESS