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SGD is an abbreviation for Stochastic Gradient Descent, a popular optimization algorithm in machine learning.
SGD是随机梯度下降法的缩写，在机器学习中是一种常用的优化算法。
The model's parameters were updated using the SGD method during each training iteration.
在每次训练迭代过程中，我们使用SGD方法更新模型参数。
In deep learning, backpropagation combined with SGD is used to minimize the cost function.
在深度学习中，通常结合反向传播与SGD来最小化损失函数。
To avoid getting stuck in local minima, we can use a variant of SGD called Momentum.
为了避免陷入局部最优解，我们可以使用SGD的一个变种——动量项。
The learning rate is a hyperparameter that needs to be carefully tuned when using SGD.
使用SGD时，学习率是一个需要谨慎调整的超参数。
Mini-batch SGD is often preferred over vanilla SGD as it offers a balance between computational efficiency and convergence speed.
相较于纯随机梯度下降法，小批量SGD在计算效率和收敛速度之间提供了较好的平衡，因此更受欢迎。
With Adaptive Learning Rate methods like AdaGrad or Adam, the impact of different features on the gradient update can be adjusted during the SGD process.
使用Adagrad或Adam等自适应学习率方法，可以在SGD过程中动态调整不同特征对梯度更新的影响。
The early stopping technique can help prevent overfitting when training neural networks with SGD.
在使用SGD训练神经网络时，早期停止技术有助于防止过拟合。
Nesterov Accelerated Gradient (NAG) is another modification to SGD that allows the optimizer to look ahead before updating weights.
Nesterov加速梯度（NAG）是对SGD的一种改进，它允许优化器在更新权重之前进行预览。
When dealing with sparse data, sparse gradient updates can significantly improve performance in comparison to standard SGD.
处理稀疏数据时，稀疏梯度更新相比于标准SGD可以显著提高性能。