why did I make it?
why did I fail?
single feature
feature mean pooling
feature max pooling
https://www.bilibili.com/video/BV1Xp4y1b7i
Target
2021-04-12 -> 2021-04-18
Date
Sleep before 1:00
Selfie
Squat * 20
2021-04-12
2021-04-13
2021-04-14
Y
2021-04-15
2021-04-16
2021-04-17
2021-04-18
Academic
Coding
git
放弃工作区修改，一定不能忘记--，否则就是切换 branch 了 。
git checkout -- filename
Bookmarks
https://www.novipnoad.com/anime/134365.html
Buying
Ideas
Machine Learning
precision and recall
Ground Truth \ Prediction
Positive
Negtive
True
TP
TN
False
FP
FN
precision = T P T P + F P \text{precision} = \frac{TP}{TP + FP}
precision = T P + F P T P
recall = T P T P + F N \text{recall} = \frac{TP}{TP + FN}
recall = T P + F N T P
去医院看病
有病，诊断有病
TP
真阳性
没病，诊断有病
FP
假阳性
误报
有病，诊断没病
FN
假阴性
漏报
没病，诊断没病
TN
真阴性
Math
Normal Distribution
对于正态分布 X ∼ N ( μ , σ ) X \sim \mathcal{N}(\mu, \sigma) X ∼ N ( μ , σ ) 概率密度函数 为 f ( x ) = 1 2 π σ exp ( − ( x − μ ) 2 2 σ 2 ) f(x) = \frac{1}{\sqrt{2 \pi} \sigma} \exp \left(-\frac{(x-\mu)^{2}}{2 \sigma^{2}}\right) f ( x ) = 2 π σ 1 exp ( − 2 σ 2 ( x − μ ) 2 )
如果 X ∼ N ( μ , σ ) X \sim \mathcal{N}(\mu, \sigma) X ∼ N ( μ , σ ) X − μ σ ∼ N ( 0 , 1 ) \frac{X - \mu}{\sigma} \sim \mathcal{N}(0, 1) σ X − μ ∼ N ( 0 , 1 ) 普通正态分布如何转换到标准正态分布 ）
如果 X ∼ N ( 0 , 1 ) X \sim \mathcal{N}(0, 1) X ∼ N ( 0 , 1 ) f 1 ( x ) = 1 2 π σ exp ( − ( x − μ ) 2 2 σ 2 ) = 1 2 π exp ( − x 2 2 ) f_1(x)=\frac{1}{\sqrt{2 \pi} \sigma} \exp \left(-\frac{(x-\mu)^{2}}{2 \sigma^{2}}\right)=\frac{1}{\sqrt{2 \pi}} \exp \left(-\frac{x^{2}}{2}\right) f 1 ( x ) = 2 π σ 1 exp ( − 2 σ 2 ( x − μ ) 2 ) = 2 π 1 exp ( − 2 x 2 )
如果 X ∼ N ( 0 , σ ) X \sim \mathcal{N}(0, \sigma) X ∼ N ( 0 , σ ) f 2 ( x ) = 1 2 π σ exp ( − ( x − μ ) 2 2 σ 2 ) = 1 2 π σ exp ( − x 2 2 σ 2 ) f_2(x)=\frac{1}{\sqrt{2 \pi} \sigma} \exp \left(-\frac{(x-\mu)^{2}}{2 \sigma^{2}}\right)=\frac{1}{\sqrt{2 \pi} \sigma} \exp \left(-\frac{x^{2}}{2 \sigma^{2}}\right) f 2 ( x ) = 2 π σ 1 exp ( − 2 σ 2 ( x − μ ) 2 ) = 2 π σ 1 exp ( − 2 σ 2 x 2 )
f 2 ( x ) f 1 ( x ) = 1 2 π σ exp ( − x 2 2 σ 2 ) 1 2 π exp ( − x 2 2 ) = 1 σ exp ( − x 2 2 ( 1 σ 2 − 1 ) ) = 1 σ [ exp ( − x 2 2 ) ] 1 σ 2 − 1 = 1 σ [ 2 π f 1 ( x ) ] 1 σ 2 − 1 = 1 σ ( 2 π ) 1 − σ 2 2 σ 2 [ f 1 ( x ) ] 1 σ 2 − 1 \frac{f_2(x)}{f_1(x)} = \frac{\frac{1}{\sqrt{2 \pi} \sigma} \exp \left(-\frac{x^{2}}{2 \sigma^{2}}\right)}{\frac{1}{\sqrt{2 \pi}} \exp \left(-\frac{x^{2}}{2}\right)} = \frac{1}{\sigma}\exp \left( -\frac{x^{2}}{2} \left( \frac{1}{\sigma^2}-1\right) \right) = \frac{1}{\sigma}\left[\exp \left( -\frac{x^{2}}{2} \right)\right] ^ {\frac{1}{\sigma^2}-1}\\
= \frac{1}{\sigma}\left[ \sqrt{2 \pi}f_1(x) \right] ^ {\frac{1}{\sigma^2}-1} = \frac{1}{\sigma}(2 \pi)^{\frac{1 - \sigma^2}{2\sigma^2}}\left[f_1(x)\right]^{\frac{1}{\sigma^2} - 1}
f 1 ( x ) f 2 ( x ) = 2 π 1 exp ( − 2 x 2 ) 2 π σ 1 exp ( − 2 σ 2 x 2 ) = σ 1 exp ( − 2 x 2 ( σ 2 1 − 1 ) ) = σ 1 [ exp ( − 2 x 2 ) ] σ 2 1 − 1 = σ 1 [ 2 π f 1 ( x ) ] σ 2 1 − 1 = σ 1 ( 2 π ) 2 σ 2 1 − σ 2 [ f 1 ( x ) ] σ 2 1 − 1
f 2 ( x ) = 1 σ ( 2 π ) 1 − σ 2 2 σ 2 [ f 1 ( x ) ] 1 σ 2 f_2(x) = \frac{1}{\sigma}(2 \pi)^{\frac{1 - \sigma^2}{2\sigma^2}}\left[f_1(x)\right]^{\frac{1}{\sigma^2}}
f 2 ( x ) = σ 1 ( 2 π ) 2 σ 2 1 − σ 2 [ f 1 ( x ) ] σ 2 1
Sentences
"Maturity is learning how to start when you feel like procrastinating and learning how to listen when you feel like talking."
From James Clear james@jamesclear.com via h.ckdlv.net
"Solve big problems early.
From James Clear james@jamesclear.com via h.ckdlv.net
Travel
