Optimization of Machine Learning
機器學習就是須要找到模型的鞍點，也就是最優勢。由於模型不少時候並非徹底的凸函數，因此若是沒有好的優化方法可能會跑不到極值點，或者是局部極值，甚至是偏離。因此選擇一個良好的優化方法是相當重要的。首先是比較常規的優化方法：梯度降低。如下介紹的這些算法都不是用於當個算法，能夠試用於能可微的全部算法。git Gradient Descent 常見會用在logistics regression或者是line
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相關文章
- 1. Optimization of Machine Learning
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- 8. Third week of machine learning on Coursera
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- 10. Machine Learning Course 4 Selection of Model