Andrew Ng 's machine learning lecture note (16)

FrostMonarch

于 2018-06-11 11:54:07 发布

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分类专栏： Andrew Ng 's Note

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本文探讨了面对大规模数据集时如何提高计算效率的方法，包括梯度下降法的改进如随机梯度下降和小批量梯度下降等，并介绍了合成大规模数据集的两种方式：通过为现有数据添加扰动来创建新数据以及组合多种元素生成新数据。

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When facing the large scale data sets, it's necessary to compute more efficiently. Before we need to use a more computational method, we'd better sanity check first.

There are several improving gradient decent method.

(1)Stochastic gradient decent

(2)Mini-batch gradient decent

（3）Synthesis large scale data

As we know that in machine learning, large scale data will be very beneficial. There're 2 ways to synthesize data.

First, we can add disturbance to one data to create new data(Remember random disturbance will not be helped)

Second, we can just combine several elements together, for example in text detection, we can just download font styles form the Internet then we can add backgrounds to them to crete new data.