The Footsteps of My Dear Love 扒谱竟然产生心流体验了，非常快乐。全曲进程 1:51 / 4:08，好消息是二重唱基本完成，有很多重复乐段相当省力，还成功听出了上次不知道为什么没听清的女低（可能是小音箱音质不太行，换成电脑就好了），然而弦乐变奏大约还有百八十个 😂
把混乱简谱转录成 musescore，发现 1. 我的节奏感很强但常常心里没有书面音符时值的直觉，总得打拍子数时值才能写下来；这其实也对视唱读谱造成了一定障碍，2. 正式的电子乐谱真的对反复琢磨很有帮助，因为可以轻易的实行古尔德编曲法（？），也就是听其它声部重播的同时，哼唱还没确定的声部，找总体和声的感觉。
把弦乐安插给了男高，把之前设想的一个复调点子安排给了女高和女低对位（？），回头想想男低怎么写。女高可能会导致整个曲子需要转调，否则她们要不然得唱 high C 要不然得唱中央 C 下方的 F 😂 或者让女高和女低互相交接主旋律
🌟 Sunday, April 26, 2026 🌟
Progress Update
Day 115 of Year 3 | Day 846 overall since Jan 1, 2024
📚 Daily Reading
✅ freeCodeCamp News — 1 article
✅ Daily.dev — 1 article
💻 Learning Focus
• freeCodeCamp: Python ~ Mastering String Slicing 🔪✨
Leveling up with string slicing—because the more you refine your skills, the cleaner your code becomes.
#Python #StringSlicing #CodingPractice #LearnInPublic #DevJourney #TechGrowth #CodeLife #Consistency
Day 87 of learning AI/ML
I studied Inference for regression slope
• Inference about slope (linear regression)
• Conditions for valid inference
• Confidence interval for slope
• t-statistic for slope
• Using p-value to conclude
Day 86 of learning AI/ML
I studied Chi-square tests (tables & association)
• Frequency & contingency tables
• Chi-square test for homogeneity
• Chi-square test for independence
• Testing relationships between variables
Day 85 of learning AI/ML
I studied Chi-square tests (categorical data)
• Inference for categorical data
• Chi-square distribution (intro)
• Goodness-of-fit test
• Chi-square statistic
• Interpreting results
Day 84 of learning AI/ML
I studied Comparing means
• Statistical significance (real example)
• Difference of sample means distribution
• Confidence interval for difference of means
• Hypothesis test for difference of means
Day 83 of learning AI/ML
I studied Comparing population proportions
• Comparing two population proportions
• Hypothesis testing for proportions
• Interpreting statistical significance
• Drawing conclusions from experiments
Day 82 of learning AI/ML
I studied Hypothesis testing (summary)
• Hypothesis testing & p-values
• One-tailed vs two-tailed tests
• z vs t statistics
• Small vs large sample tests
• Proportion hypothesis testing
Day 81 of learning AI/ML
I studied Hypothesis testing for a mean
• Writing hypotheses (mean)
• Conditions for t-test
• When to use z vs t
• Calculating t-statistic
• Finding & comparing p-values
• Making conclusions from test
Day 80 of learning AI/ML
I studied Hypothesis testing for proportions
• Constructing null & alternative hypotheses
• Conditions for z-test (proportion)
• Calculating p-value from z-score
• Making conclusions from test results
No-ghost theorem
LogicLuminaryBill
Chitra