Index
Symbols
- __call__(), Static Unrolling Through Time
- ε-greedy policy, Exploration Policies
- ε-insensitive, SVM Regression
- χ 2 test (see chi square test)
- ℓ 0 norm, Select a Performance Measure
- ℓ 1 and ℓ 2 regularization, ℓ1 and ℓ2 Regularization-ℓ1 and ℓ2 Regularization
- ℓ 1 norm, Select a Performance Measure, Lasso Regression, Decision Boundaries, Adam Optimization, Avoiding Overfitting Through Regularization
- ℓ 2 norm, Select a Performance Measure, Ridge Regression-Lasso Regression, Decision Boundaries, Softmax Regression, Avoiding Overfitting Through Regularization, Max-Norm Regularization
- ℓ k norm, Select a Performance Measure
- ℓ ∞ norm, Select a Performance Measure
A
- accuracy, What Is Machine Learning?, Measuring Accuracy Using Cross-Validation-Measuring Accuracy Using Cross-Validation
- actions, evaluating, Evaluating Actions: The Credit Assignment Problem-Evaluating Actions: The Credit Assignment Problem
- activation functions, Multi-Layer Perceptron and Backpropagation-Multi-Layer Perceptron and Backpropagation
- active constraints, SVM Dual Problem
- actual class, Confusion Matrix
- AdaBoost, AdaBoost-AdaBoost
- Adagrad, AdaGrad-AdaGrad
- Adam optimization, Adam Optimization-Adam Optimization, Adam Optimization
- adaptive learning rate, AdaGrad
- adaptive moment optimization, Adam Optimization
- agents, Learning to Optimize Rewards
- AlexNet architecture, AlexNet-AlexNet
- algorithms
- preparing data for, Prepare the Data for Machine Learning Algorithms-Select and Train a Model
- AlphaGo, Reinforcement Learning, Introduction ...
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