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There are two main types of regularization: L1 regularization (also known as Lasso) and L2 regularization (also known as Ridge). **L1 regularization** adds the sum of the absolute values of the coefficients to the loss function. This encourages sparsity, meaning that some of the coefficients will be driven to zero, effectively performing feature selection. **L2 regularization**, on the other hand, adds the sum of the squares of the coefficients to the loss function. This shrinks the coefficients towards zero but doesn't typically drive them all the way to zero. L2 regularization helps to reduce the impact of highly correlated features and improve the model's generalization performance.
Another important aspect of the *IITownscaper developer* mindset is *iteration*. This means building, testing, refining, and starting over. It's not about perfection from the start. They are constantly tweaking, making adjustments, and learning from user feedback. They understand that the game will evolve over time. This iterative process allows them to stay connected to their audience and make the game better. It helps keep the game fresh and engaging. This method is the engine that drives continuous improvement.
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