<exercise.1-18>
????????Dot Product is connected to Cosine Similarity
normalize with unit vector..
유닛백터로 노멀라이즈를 하면 닷 프로덕트를 이용하는 것과 마찬가지로 비교를 같은 수준에서 할 수 있게 해줌.
논문표절 잡을 수 있음. 코사인 유사도 이용해서
아래 공식을 이해할 것. 내적과 유사도?????????
<source: JE Yang>
study more about cosine similarity and dot product
<exercise1-19: Euclidean distance>
-> shortest distance. moves diaboticaly
1. Manhattan distance: only moves right/left or up/down like how a taxi moves manhattan's blocks
- Guardian distance
<source: JE Yang>
<exercise.1-20: Squared Error>
yhat : predicted value of the model
<exercise.1-21: Mean Squared Error>
<exercise.1-22: making List & accessing the elements>
<exercise.1-23: Modifying the Elements of a List>
<exercise.1-24:
<exercise.1-25: Mean Subtraction(2)>
one of data-preprocessing methods
make average of the data to zero
<exercise.1-26: Variance and Standard Deviation>
when you hear Variance you gotta think of "Mean of Squared Deviations" first!!!?
<exercise.1-27: Standardization(2)>
also says Z-Score Normalization.
** PEP8 ** READ IT!!!
it's very important to make the code CLEAN so that every other coders can recognize what i'm suggesting with the code
<exercise.1-28: Hadamard Product(2)>
<exercise.1-29: list() and append()>
<exercise.1-30: Hadamard Product(3)>
hadamard using list and append
** cosine similarity and dot product
<exercise.1-31: Vector Norm(2)>
<exercise.1-32: Making Unit Vectors(2)>
<exercise.1-33: Dot Product(2)>
<exercise.1-34: Euclidean Distance(2)>
<exercise.1-35: Mean Squared Error(2)>
<exercise.1-36: accessing elements of a list using "for" loop>
<exercise.1-37: sum of elements of a list>
<exercise.1-38: count the number of Iterations>
<exercise.1-39: sum of 1 to 100>
<exercise.1-40: make a list of 1 to 100>
<exercise.1-41: create a list of 100 zeros>
<exercise.1-42: accessing the elements of a list using For loop(2)>
make a Data Index using For loop, len(), range()
<exercise.1-43: modifying the elements of a list using For loop>
<exercise.1-44: accessing two lists>
