Optimizers in Detail: Optimizers | AI Knowledge Hub

Optimizers in Detail

From SGD to AdamW: selecting the optimal optimizer involves considering techniques like Stochastic Gradient Descent and more modern adaptive optimizers such as Adam and AdamW. The correct choice and tuning of an optimizer are crucial for successful model training.

Optimizers dictate how neural networks update their weights during learning. From classic SGD to contemporary adaptive optimizers like Adam and AdamW – the appropriate selection and configuration of the optimizer is critical for successful model training.

Principle: Adapts Learning Rate Based on Historical Gradients

Advantages: Automatic reduction in learning rate.

Disadvantages: The learning rate may become too small.

Principle: Fixed Learning Rate

Application: Simple tasks, Adam.

Principle: Reduction over certain epochs.

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