Machine Learning Guide
A machine learning podcast teaching the fundamentals of machine learning and artificial intelligence. It covers intuition, models, neural networks, math, languages, frameworks, etc. Where visual ML resources provide the trees, I provide the forest. Podcasts are a great supplement during exercise, commute, chores, etc.
MLG 028 Hyperparameters 2
Feb 04, 2018
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Hyperparameters part 2: hyper-search, regularization, SGD optimizers, scaling
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Show Notes
- Hyper optimization
- GridSearch, RandomSearch
- Bayesian Optimization
- Regularization: Dropout, L2, L1
- DNNs = Dropout
- L2 = most common
- L1 = sparsity (zeros) & feature-selection (rarer circumstances)
- Optimizers (SGD): Momentum -> Adagrad -> RMSProp -> Adam -> Nadam
- Initializers: Zeros, Random Uniform, Xavier
- Scaling
- Feature-scaling: MinMaxScaler, StandardScaler, RobustScaler
- Features + inter-layer: Batch Normalization