****GRADIENT DECENT-MACHINE LEARNING*** In measurements, straight relapse is a direct way to deal with demonstrate the connection between a reliant variable and at least one free factor. Let X be the autonomous variable and Y be the reliant variable. We will characterize a straight connection between these two factors as follows: Y = mX + c This is the condition for a line that you concentrated on in secondary school. m is the incline of the line and c is the y block. Today we will utilize this condition to prepare our model with a given dataset and anticipate the worth of Y for some random worth of X. Our test today is to decide the worth of m and c, with the end goal that the line relating to those values is the best fitting line or gives the base blunder. One method for tackling this issue is to utilize Gradient Decent (The reference here contain more subtleties of Gradient respectable and test code, attempt not use numpy in this inquiry): The Algorithm of slope good to find m and c is : 1. Set starting variable. m=0 and c=0, Learning rate L=0.001, number of emphasis. 2. Compose a for circle, in this circle, go over all pair (xi , yi): (a) work out y pred I = xi ∗ m + b (b) work out xi(y pred I − yi), and store it in list Dm (c) work out (y pred I − yi), and store it in list Dc 3. work out the normal for list Dm and Dc equivalent to dm and dc 4. update m by: m = m − L × dm 5. update c by: c = c − L × dc Use python only
****GRADIENT DECENT-MACHINE LEARNING***
In measurements, straight relapse is a direct way to deal with demonstrate the connection between a reliant variable and at least one free factor. Let X be the autonomous variable and Y be the reliant variable. We will characterize a straight connection between these two factors as follows: Y = mX + c This is the condition for a line that you concentrated on in secondary school. m is the incline of the line and c is the y block. Today we will utilize this condition to prepare our model with a given dataset and anticipate the worth of Y for some random worth of X. Our test today is to decide the worth of m and c, with the end goal that the line relating to those values is the best fitting line or gives the base blunder. One method for tackling this issue is to utilize Gradient Decent (The reference here contain more subtleties of Gradient respectable and test code, attempt not use numpy in this inquiry): The
1. Set starting variable. m=0 and c=0, Learning rate L=0.001, number of emphasis.
2. Compose a for circle, in this circle, go over all pair (xi , yi): (a) work out y pred I = xi ∗ m + b (b) work out xi(y pred I − yi), and store it in list Dm (c) work out (y pred I − yi), and store it in list Dc 3. work out the normal for list Dm and Dc equivalent to dm and dc
4. update m by: m = m − L × dm
5. update c by: c = c − L × dc
Use python only
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