Andrew Ng 's machine learning lecture note (16)

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。

摘要生成于 C知道 ,由 DeepSeek-R1 满血版支持, 前往体验 >

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.
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值