A detailed explanation and difference between L1 Regularization and L2 Regularization. It also explains in detail the function of regularization in the loss function and explains why regularization can prevent overfitting and improve generalization.

---

Preface

正则化 is an important technical means that can help us improve the generalization ability of the model, 减少过拟合 phenomenon. L1正则化 and L2正则化 are two commonly used regularization methods. They have their own characteristics and are suitable for different types of problems.

1. What is regularization?

正则化（Regularization） is a technical means used for 防止过拟合, 提高模型泛化能力 in machine learning and deep learning algorithms.

2. What is L1 Regularization?

L1正则化 refers to 各个元素的绝对值之和 in the weight vector w. The main function of L1 regularization is 引导模型更加关注那些绝对值较大的权重, thus preventing overfitting to a certain extent. In the field of machine learning, L1 regularization is usually used for models in which most of the weights are zero, such as Lasso regression. 稀疏模型

3. What is L2 Regularization?

L2 regularization refers to the 平方和然后再求平方根 of each element in the weight vector w. The main effect of L2 regularization is 减少模型的复杂性, and thus 防止过拟合 to a certain extent. In the field of machine learning, L2 regularization is often usedRidge回归等模型.

4. Application of regularization in loss function

1. You see regularization in the loss function because regularization is used控制模型复杂度和防止过拟合的技术.

2. Regularization is passed in the loss function引入附加项（正则化项) to penalize the complexity of the model or control the size of the parameters. This can keep the model on the training data有良好拟合的同时，避免过度拟合 and improve the generalization ability of the model.

3. The basic principle of regularization is to prevent the model from in the training data by惩罚模型的复杂度或参数的大小. Regularization, therebyand prediction accuracy on unseen data. 过度适应噪声或不相关的特征使模型倾向于选择更简单的参数设置或稀疏的特征提高模型的泛化能力

4. Regularization can be viewed as是对损失函数中的某些参数做一些限制, so that during training 引导模型更加关注那些不太可能产生过拟合的参数.

---