Machine Learning - Day2 -ML Intro_1
作者:互联网
文章目录
Edible Instruction
- Too many formulations and equations, so screen shots of PPT is necessary.
- My mathematics sucks, so you 'll see many mathematical knowledge interspersed in my all ML learning session.
- Don’t be afraid of any mathematical symbol, just BAIDU the definition.
What’s Machine Learning?
Machine Learning = Looking for function
What’s the function in math?
In mathematics, a function is a binary relation between two sets that associates to each element of the first set exactly one element of the second set.
The reality of scene:
Different Types Of Functions
Regression
Regression : The function outputs a scalar
Classification
Classification: Given options(classes), the function outputs the correct one.
So Alpha Go is a classification function, 19X19 classes to output a position
Structured Learning
Structured Learning: Create something with structure( image, doctument, video)
Ask machine to create the things.
Example application
Reminder: the function of sample is a conjecture
Estimating the views of Youtube channel tomorrow, base on the history of views
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Function with unknown parameters
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Define Loss from Training Data (Important!!!)
Loss is a function of parameters L(b,w), and comes from the Training DATA.
Loss: how good a set of values is.
So, if Loss value is large, the parameters are bad.
Suppose L(0.5k, 1)
Label : The true value is label!!!
∑ \sum ∑ : Sum things up (called Sigma)
Refer to Article
Sum all e n e_n en up
Choose MAE to calculate on the PPT, and MSE on the homework
probability distribution (概率分布)
Cross entropy (交叉熵)
The graph of true data
3. Optimization
arg min f(x) : the value of x when f(x) have the minimum value. arg =
argument, arg min: the argument of the minimum of target function
Gradient Descent:
Postulate we only have one unknown parameter
Pick an initial value W 0 W^0 W0 (Blind guess here)
Differential coefficient(微分), please refer to 知乎acticle
and Differential Equations Solution Guide
Learning rate : η \eta η
Update
标签:function,Loss,What,ML,value,Machine,Intro,Learning 来源: https://blog.csdn.net/landian0531/article/details/117522559