Notes and resources: ocdevel.com/mlg/28
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More hyperparameters for optimizing neural networks. A focus on regularization, optimizers, feature scaling, and hyperparameter search methods.
Hyperparameter Search Techniques
- Grid Search involves testing all possible permutations of hyperparameters, but is computationally exhaustive and suited for simpler, less time-consuming models.
- Random Search selects random combinations of hyperparameters, potentially saving time while potentially missing the optimal solution.
- Bayesian Optimization employs machine learning to continuously update and hone in on efficient hyperparameter combinations, avoiding the exhaustive or random nature of grid and random searches.
Regularization in Neural Networks
- L1 and L2 Regularization penalize certain parameter configurations to prevent model overfitting; often smoothing overfitted parameters.
- Dropout randomly deactivates neurons during training to ensure the model doesn’t over-rely on specific neurons, fostering better generalization.
Optimizers
- Optimizers like Adam, which combines elements of momentum and adaptive learning rates, are explained as vital tools for refining the learning process of neural networks.
- Adam, being the most sophisticated and commonly used optimizer, improves upon simpler techniques like momentum by incorporating more advanced adaptative features.
Initializers
- The importance of weight initialization is underscored with methods like uniform random initialization and the more advanced Xavier initialization to prevent neural networks from starting in 'stuck' states.
Feature Scaling
- Different scaling methods such as standardization and normalization are used to scale feature inputs to small, standardized ranges.
- Batch Normalization is highlighted, integrating scaling directly into the network to prevent issues like exploding and vanishing gradients through the normalization of layer outputs.
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