17. Large scale machine learning

作者: 玄语梨落 | 来源:发表于2021-01-30 10:06 被阅读0次

Large scale machine learning

Learining with large datasets

Stochastic gradient descent

Batch gradient descent:

$J_{train}(\theta)=\frac{1}{2m}\sum\limits_{i=1}^m(h_\theta(x^{(i)}-y^{(i)})^2$
Repeat{
$\theta_j:=\theta_j-\alpha\frac{1}{m}\sum\limits_{i=1}^m(h_\theta(x^{(i)}-y^{(i)})x_j^{(i)}$
}

Stochastic gradient descent:

$cost(\theta,(x^{)i)},y^{(i)}))=\frac{1}{2}(h_\theta(x^{(i)}-y^{(i)})^2$
$J_{train}(\theta)=\frac{1}{m}\sum\limits_{i=1}^m cost(\theta,(x^{)i)},y^{(i)}))$

Randomly shaffle dataset
Repeat{for{}}

Mini-batch gradient descent

Mini-batch gradient descent: Use $b$ examples in each iteration.

$b$ = mini-batch size

$\Theta_j:=\Theta_j-\alpha\frac{1}{10}\sum_{k=i}^{i+9}(h_\theta(x^{(k)})-y^{(k)})x_j^{(k)}$

Stochastic gradient descent convergence

Checking for convergence:

Batch gradient descent:
Stochastic gradient descent: Every 1000 iterations (say), plot $cost(\theta,(x^{(i)},y^{(i)}))$ averaged ove the last 1000 examples processed by algorithm.

For Stochastic gradient descent: Learning rate $\alpha$ istypically held constant. Can slowly decrease $\alpha$ over time if we want $\theta$ to converge. (E.g. $\alpha = \frac{const1}{iterationNmuber +const2}$ )

Online learning

operate one data once.

Predicte CTR (click through rate)

Map-reduce and data parallelism

divide all work into many parts and calculate them at the same time with different machine.

Map-reduce and summation over the training set:

Many learining algorithms can be expressed as computing sums of functions over the training set.

Multi-core machines:

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