随笔分类 - machine learning 相关
机器学习相关知识
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）
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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
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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{
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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
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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
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摘要：Notation: m = Number of training examples x's = "input" variable / features y's = "output" variable / "target" variable (x, y) - one training example
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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
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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
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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
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