摘要: Hypothesis: \[{h_\theta }\left( x \right) = {\theta ^T}x = {\theta _0} + {\theta _1}{x_1} + {\theta _2}{x_2} + ... + {\theta _n}{x_n}\] 参数(Parameters) 阅读全文
posted @ 2018-10-22 21:13 qkloveslife 阅读(833) 评论(0) 推荐(0) 编辑
摘要: Multiple features (variables) Size x1 Number of bedrooms x2 Number of floors x3 Age of home(year) x4 Price y Notation: n = number of features x(i) = i 阅读全文
posted @ 2018-10-22 20:17 qkloveslife 阅读(242) 评论(0) 推荐(0) 编辑
摘要: 梯度下降算法 重复直到收敛{ \[{\theta _j}: = {\theta _j} - \alpha \frac{\partial }{{\partial {\theta _j}}}J\left( {{\theta _0},{\theta _1}} \right)\left( {for{\rm{ 阅读全文
posted @ 2018-10-22 19:30 qkloveslife 阅读(983) 评论(0) 推荐(0) 编辑
摘要: Have some function J(θ0, θ1), generally J(θ0, θ1,θ2, θ3,..., θn) Want: \[\mathop {\min }\limits_{{\theta _0},{\theta _1}} J\left( {{\theta _0},{\theta 阅读全文
posted @ 2018-10-22 12:43 qkloveslife 阅读(500) 评论(0) 推荐(0) 编辑
摘要: Training Set 训练集 Hypothesis: \[{h_\theta }\left( x \right) = {\theta _0} + \theta {x}\] Notation: θi's: Parameters θi's: 参数 How to choose θi's? 如何选择θi 阅读全文
posted @ 2018-10-21 11:32 qkloveslife 阅读(3042) 评论(0) 推荐(0) 编辑
摘要: Notation: m = Number of training examples x's = "input" variable / features y's = "output" variable / "target" variable (x, y) - one training example 阅读全文
posted @ 2018-10-21 07:30 qkloveslife 阅读(215) 评论(0) 推荐(0) 编辑
摘要: 无监督学习 Unsupervised learning allows us to approach problems with little or no idea what our results should look like. We can derive structure from data 阅读全文
posted @ 2018-10-16 18:07 qkloveslife 阅读(356) 评论(0) 推荐(0) 编辑
摘要: 监督学习 (Supervised Learning) "right answers" given (监督学习的特点是:给定“正确答案”) In supervised learning, we are given a data set and already know what our correct 阅读全文
posted @ 2018-10-16 00:03 qkloveslife 阅读(243) 评论(0) 推荐(0) 编辑
摘要: 机器学习的定义 定义1 Arthur Samuel (1959). Machine Learning: Field of study that gives computers the ability to learn without being explicitly programmed. Arth 阅读全文
posted @ 2018-10-15 17:11 qkloveslife 阅读(470) 评论(0) 推荐(0) 编辑
摘要: 转自 https://www.cnblogs.com/rj81/p/5933838.html 在日常工作中经常遇见在文本中提取特定位置字符串的需求.python的正则性能好,很适合做这类字符串的提取,这里讲一下提取的技巧,正则表达式的基础知识就不说了,有兴趣的可以看re的教程. 提取一般分两种情况, 阅读全文
posted @ 2018-07-05 17:28 qkloveslife 阅读(44428) 评论(0) 推荐(1) 编辑