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                                    "content": " 是什么？A. 雅可比矩阵B. Hessian（二阶偏导矩阵）C. 协方差矩阵D. 单位矩阵"
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                                    "content": " 是什么？A. 雅可比矩阵B. Hessian（二阶偏导矩阵）C. 协方差矩阵D. 单位矩阵"
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                                    "content": "8. Hessian 有正有负特征值时，驻点是？\nA. 全局最小\nB. 鞍点\nC. 一定发散\nD. 一定是极大"
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                                    "content": " 在原点处，牛顿法一步会到哪里？A. 某个极小值B. 原点（鞍点）C. 无穷远D. 随机"
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                                    "content": "10. 同一函数上，梯度下降从鞍点附近出发通常会怎样？\nA. 同样停在鞍点\nB. 沿负曲率方向滑走\nC. 一定到全局最小\nD. 立刻发散到 NaN"
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                                    "content": "C. 永不收敛  \nD. 与 "
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                                    "bbox": [
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                                    "type": "text",
                                    "content": " 在原点处，牛顿法一步会到哪里？A. 某个极小值B. 原点（鞍点）C. 无穷远D. 随机"
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                                    "content": "13. 阻尼牛顿与纯牛顿的差别是?\nA. 不用 Hessian\nB. 步长  "
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                                    "content": " 的作用是？A. 把牛顿和梯度下降连起来B. 计算特征值C. 做正则化分类D. 替代损失"
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                                    "content": "17. L-BFGS 相对 BFGS 主要省了什么?\nA. 省梯度\nB. 不存  "
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                                    "type": "inline_equation",
                                    "content": "n \\times n"
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                                    "type": "text",
                                    "content": "  矩阵，只记最近 m 对向量\nC. 省掉线搜索\nD. 变成一阶"
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                                    "content": "  矩阵，只记最近 m 对向量\nC. 省掉线搜索\nD. 变成一阶"
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                                    "content": "18. 深度学习训练很少用纯牛顿法，首要原因是？A. 牛顿法数学错误B. Hessian 太大，存/求逆代价过高"
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                                    "content": " C.立刻判定已收敛D.把学习率乘10"
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                                    "content": " 对上题做牛顿法，更可能冲向？A. "
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                                    "content": " D. 随机"
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                                    "content": "24. sklearn 逻辑回归 solver='lbfgs' 属于?\nA. 纯 SGD\nB. 有限内存拟牛顿"
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                                    "content": " 对上题做牛顿法，更可能冲向？A. "
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                                    "content": "x = 1"
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                                    "content": "24. sklearn 逻辑回归 solver='lbfgs' 属于?\nA. 纯 SGD\nB. 有限内存拟牛顿"
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                                    "content": "C. 牛顿-共轭梯度的别名\nD. 遗传算法"
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                                    "content": "29. 相机标定、Bundle Adjustment 里常用的高斯牛顿 / LM，本质是？\nA. 随机搜索\nB. 最小二乘上的牛顿类方法\nC. 纯 Adam\nD. 离散枚举"
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                                    "content": "30. 下列哪句概括今天最准确？\nA. 牛顿法已过时，不必学\nB. 牛顿用法是曲率；问题太大就估曲率或退回一阶"
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                                    "content": "C. 牛顿-共轭梯度的别名\nD. 遗传算法"
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                                    "content": " 梯度。这和后面哪一课直接衔接？A. 排列组合B. 线性方程组与高斯消元C. 贝叶斯D. 图论"
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                                    "content": "27. 为什么 mini-batch 训练更不适合精确牛顿?\nA. batch 没有梯度\nB. 噪声梯度让 Hessian 估计更不稳定\nC. batch 一定非凸\nD. GPU 算不了矩阵"
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                                    "content": "28. 拟牛顿要求近似矩阵 B 尽量保持正定，是为了？\nA. 让步长必须为 1\nB. 保证搜索方向是下降方向\nC. 让特征值全为负\nD. 减少内存"
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