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| title: "Optimizers in Neural Networks" | |
| author: "Sébastien De Greef" | |
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| ## Introduction to Optimizers | |
| Optimizers are crucial for training neural networks by updating the network's weights based on the loss gradient. They impact the training speed, quality, and the model's final performance. | |
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| ## Role of Optimizers | |
| - **Function**: Minimize the loss function | |
| - **Mechanism**: Iteratively adjust the weights | |
| - **Impact**: Affect efficiency, accuracy, and model feasibility | |
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| ## Gradient Descent | |
| - **Usage**: Basic learning tasks, small datasets | |
| - **Strengths**: Simple, easy to understand | |
| - **Caveats**: Slow convergence, sensitive to learning rate settings | |
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| ## Stochastic Gradient Descent (SGD) | |
| - **Usage**: General learning tasks | |
| - **Strengths**: Faster than batch gradient descent | |
| - **Caveats**: Higher variance in updates | |
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| ## Momentum | |
| - **Usage**: Training deep networks | |
| - **Strengths**: Accelerates SGD, dampens oscillations | |
| - **Caveats**: Additional hyperparameter (momentum) | |
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| ## Nesterov Accelerated Gradient (NAG) | |
| - **Usage**: Large-scale neural networks | |
| - **Strengths**: Faster convergence than Momentum | |
| - **Caveats**: Can overshoot in noisy settings | |
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| ## Adagrad | |
| - **Usage**: Sparse data problems like NLP and image recognition | |
| - **Strengths**: Adapts the learning rate to the parameters | |
| - **Caveats**: Shrinking learning rate over time | |
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| ## RMSprop | |
| - **Usage**: Non-stationary objectives, training RNNs | |
| - **Strengths**: Balances decreasing learning rates | |
| - **Caveats**: Still requires learning rate setting | |
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| ## Adam (Adaptive Moment Estimation) | |
| - **Usage**: Broad range of deep learning tasks | |
| - **Strengths**: Efficient, handles noisy/sparse gradients well | |
| - **Caveats**: Complex hyperparameter tuning | |
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| ## AdamW | |
| - **Usage**: Regularization heavy tasks | |
| - **Strengths**: Better generalization than Adam | |
| - **Caveats**: Requires careful tuning of decay terms | |
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| ## Conclusion | |
| Choosing the right optimizer is crucial for training efficiency and model performance. | |
| Each optimizer has its strengths and is suited for specific types of tasks. | |