- Deep Learning; basic architectures (MLP, RNN, LSTM, GRU, ResNet), regularizers, and normalization
- Machine Learning; tree based (Decision Trees, Random Forest, GBM), bayesian techniques, linear families, and kernel methods
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Basic DL Architectures, Tree's, and Matrix Methods
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