LaTex公式

$$ 
x_i^2 
$$

\[ x_i^2 \]

$$ 
\log_2 x 
$$

\[ \log_2 x \]

$$ 
10^{10} 
$$

\[ 10^{10} \]

$$
 \{1+2\} 
$$

\[ \{1+2\} \]

$$
\frac{1+1}{2}+1
$$

\[ \frac{1+1}{2}+1 \]

$$
\sum_1^n
$$

\[ \sum_1^n \]

$$
\int_1^n
$$

\[ \int_1^n \]

$$
lim_{x\to\infty}
$$

\[ lim_{x\to\infty} \]

$$
\begin{matrix}
        1 & x & x^2 \\
        1 & y & y^2 \\
        1 & z & z^2 \\
\end{matrix}
$$

\[ \begin{matrix} 1 & x & x^2 \\ 1 & y & y^2 \\ 1 & z & z^2 \\ \end{matrix} \]

$$
h(\theta) = \sum_{j=0}^n\theta_jx_j
$$

\[ h(\theta) = \sum_{j=0}^n\theta_jx_j \]

$$
\frac{\partial J(\theta)}{\partial\theta_j} = -\frac{1}{m}\sum_{i=0}^m(y^i-h_\theta(x^i))x_j^i
$$

\[ \frac{\partial J(\theta)}{\partial\theta_j} = -\frac{1}{m}\sum_{i=0}^m(y^i-h_\theta(x^i))x_j^i \]

$$
f(n) = 
    \begin{cases}
    n/2,  & \text{if $n$ is even} \\
    3n+1, & \text{if $n$ is odd} 
    \end{cases}
$$

\[ f(n) = \begin{cases} n/2, & \text{if $n$ is even} \\ 3n+1, & \text{if $n$ is odd} \end{cases} \]

$$
\left\{
    \begin{array}{}
        a_1x+b_1y+c_1z = d_1\\
        a_2x+b_2y+c_2z = d_2\\
             a_3x+b_3y+c_3z = d_3
    \end{array}
\right.
$$

\[ \left\{ \begin{array}{} a_1x+b_1y+c_1z = d_1\\ a_2x+b_2y+c_2z = d_2\\ a_3x+b_3y+c_3z = d_3 \end{array} \right. \]

$$
X = \left(
    \begin{matrix}
        x_{11} &x_{12}&\cdots&x_{1d}\\
        x_{21} &x_{22}&\cdots&x_{2d}\\
        \vdots&\vdots&\ddots&\vdots\\
        x_{m1}&x_{m2}&\cdots&x_{md}
    \end{matrix}
    \right)
  = \left(
    \begin{matrix}
        x_1^T\\
        x_2^T\\
        \vdots\\
        x_m^T\\
    \end{matrix}
      \right)
$$

\[ X = \left( \begin{matrix} x_{11} &x_{12}&\cdots&x_{1d}\\ x_{21} &x_{22}&\cdots&x_{2d}\\ \vdots&\vdots&\ddots&\vdots\\ x_{m1}&x_{m2}&\cdots&x_{md} \end{matrix} \right) = \left( \begin{matrix} x_1^T\\ x_2^T\\ \vdots\\ x_m^T\\ \end{matrix} \right) \]

$$
\begin{align}
\frac{\partial J(\theta)}{\partial \theta_j}
    & = -\frac{1}{m}\sum_{i=0}^m(y^i-h_\theta(x^i))\frac{\partial}{\partial\theta_j}(y^i-h_\theta(x^i))  \\
    & = -\frac{1}{m}\sum_{i=0}^m(y^i-h_\theta(x^i))\frac{\partial}{\partial\theta_j}(\sum_{j=0}^n\theta_jx_j^i-y^i)  \\
    & = -\frac1m\sum_{i=0}^m(y^i-h_\theta(x^i))x_i^j
 \end{align}
$$

\[ \begin{align} \frac{\partial J(\theta)}{\partial \theta_j} & = -\frac{1}{m}\sum_{i=0}^m(y^i-h_\theta(x^i))\frac{\partial}{\partial\theta_j}(y^i-h_\theta(x^i)) \\ & = -\frac{1}{m}\sum_{i=0}^m(y^i-h_\theta(x^i))\frac{\partial}{\partial\theta_j}(\sum_{j=0}^n\theta_jx_j^i-y^i) \\ & = -\frac1m\sum_{i=0}^m(y^i-h_\theta(x^i))x_i^j \end{align} \]

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转载自www.cnblogs.com/xxxxxxxxx/p/10977771.html