gradient descent gradient descent 收敛性证明 ©作者 |黄秋实 单位|香港中文大学(深圳) 研究方向 |智能电网 梯度下降是一种简单且常用的优化方法,它可以被用来求解很多可导的凸优化问题(如逻辑回归,线性回归等)。同时,梯度下降在非凸优化问题的求解中也占有一席之地。我们常听到神经网络(neural network),也常常使...
【吴恩达深度学习专栏】神经网络的编程基础(Basics of Neural Network programming)——梯度下降法(Gradient Descent),程序员大本营,技术文章内容聚合第一站。
梯度下降(Gradient Descent)是一种简单且常用的优化方法,它可以被用来求解很多可导的凸优化问题(如逻辑回归,线性回归等)。同时,梯度下降在非凸优化问题的求解中也占有一席之地。我们常听到神经网络(neural network),也常常使用梯度下降及其变种(如随机梯度下降,Adam等)来最小化经验误差(empirical loss)。 不妨设可导的...
# 需要导入模块: from NeuralNetwork import NeuralNetwork [as 别名]# 或者: from NeuralNetwork.NeuralNetwork importgradient_descent[as 别名]defmain():iflen(sys.argv) !=3:print"USAGE: python DigitClassifier"\"<path_to_training_file> <path_to_testing_file>"sys.exit(-1) training_data =Nonevali...
In deeper neural networks, particularrecurrent neural networks, we can also encounter two other problems when the model is trained with gradient descent and backpropagation. Vanishing gradients:This occurs when the gradient is too small. As we move backwards during backpropagation, the gradient continu...
gradient descent from finding the global minimum of CC, a point we'll return to explore in later chapters. But, in practice gradient descent often works extremely well, and in neural networks we'll find that it's a powerful way of minimizing the cost function, and so helping the net ...
Linden, "Inversion of neural networks by gradient descent," Parallel Computing, 270-286, 1990.Kindermann, J, Linden, A (1990) Inversion of neural networks by gradient descent. Parallel Computing 14: pp. 277-286J. Kindermann and A. Linden. Inversion of neural networks by gradient descent. ...
gradient descent 的步骤是:先选择参数的初始值,再向损失函数对参数的负梯度方向迭代更新,learning rate控制步子大小、学习速度。梯度方向是损失函数等高线的法线方向。 gradient descent 可能使参数停在损失函数的局部最小值、导数为0的点、或者导数极小的点处。线性回归中不必担心局部最小值的问题,损失函数是凸的。
gradient flow)而非梯度下降(gradient descent)来研究深度神经网络(Deep Neural Networks,DNNs),这...
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