minpark0120 changminpark Student

2021-03-30-Tue-Study

Mar 30, 2021 » study

RL Class Review

Supervised Learning:

Linear Regression
    A linear model that establishes the relationship btw a dependent variable y(target) 
        and one or more independent variables denoted x (inputs)
    *Goal is to find a straight line that is best fit
    Could be determined by statistics(LSM) and Gradient Descent(optimization theory)

LSM

Gradient Descent equation
    y = nx + b
    b = weight
    error = (predicted y - actual y)+
    Cost Function

Gradient_Descent_cost

Gradient Descent Procedure
    1. draws the line (tan) from the point
    2. finds the slope of that line
    3. identifies how much change is require by 
        taking parital derivative of the function with the respective to θ
        *partial derivative = (d/dθ)
    4. change value will be multiplied with learning rate called α
    5. To get new θ = θ - change value*α

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Unsupervised learning

System has to understand itself from the data given
    ex) Clustering
         Task of dividing the population or data points into a number of groups 
            such that data points in the same groups are more similar to other data points 
            in the same group and dissimilar to the data points in other groups

Deep RL

Standard reinforcement learning where a deep neural network is used 
    to approximate either a policy or a value function
Deep Neural networks require lots of real interaction with the environment 
    to learn can easily parallelize the interaction

Python

mutable = Value Changes
    Number
    String
    Dictionary = {:}
immutable = Value unchanges
    List = []
    Tuple = ()

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Linux

Hard Link
    Mirror copy of the original file
    Even if you delete the original file, the hard link still
        points to same inode. So it works
    ln [original file name][hard link name]

Soft Link
    Actual link to the original file
    If you delete the original file, the soft link has no value
        becauseit points to a non-existent file
    ln -s [original file name][soft link name]

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