1.
gradient descent

2.
const makeModel = (data, learningRate, iterations) !=> {
let m = 0;
let b = 0;
for (let i = 0; i < iterations; i!++) {
const mGrad =
data.reduce(
(acc, { criticScore, globalSales }) !=>
acc + (m * criticScore + b - globalSales) * criticScore,
0
) / data.length;
const bGrad =
data.reduce(
(acc, { criticScore, globalSales }) !=>
acc + (m * criticScore + b - globalSales),
0
) / data.length;
m -= mGrad * learningRate;
b -= bGrad * learningRate;
}
return { m, b, predict: (criticScore) !=> m * criticScore + b };
};

3.
6.5 million

4.
Create a function that predicts
global sales given Metacritic score.

5.
Code…

6.
y = m · x + b

7.
Sales = m · CriticScore + b

8.
m = +0.0253
b = -1.1406

9.
Code…

10.
m = +0.0253
b = -1.1406

11.
m = ???????
b = ???????

12.
m = 0
b = 0

13.
mean(
)

14.
mean(
)

15.
Cost =
n
∑
i=1
((mxi + b) − yi)2
n

16.
Cost =
n
∑
i=1
((mxi + b) − yi)2
n

17.
Cost(m, b) =
n
∑
i=1
((mxi + b) − yi)2
n

18.
Code…

19.
Cost(m, b) =
n
∑
i=1
((mxi + b) − yi)2
n

20.
Cost(m, b) =
n
∑
i=1
((mxi + b) − yi)2
n
Cost(m) =
n
∑
i=1
(mxi − yi)2
n

21.
m = 0
hint: why might this algo be called “gradient descent?”
m′ > 0

22.
m = m − m′

23.
m = -1
m′ < 0
m = m − −m′

24.
Cost =
n
∑
i=1
((mxi + b) − yi)2
n

25.
J =
n
∑
i=1
((mxi + b) − yi)2
n

26.
J =
n
∑
i=1
((mxi + b) − yi)2
n
∂J
∂m
≈
n
∑
i=1
((mxi + b) − yi)x
n

27.
Questions?

28.
Code…

29.
m = 0
m′ > 0

30.
b = 0
b′ > 0

31.
J =
n
∑
i=1
((mxi + b) − yi)2
n

32.
J =
n
∑
i=1
((mxi + b) − yi)2
n
∂J
∂b
≈
n
∑
i=1
((mxi + b) − yi)
n

33.
Code…

34.
1000000

35.
Code…

36.
“Learning Rate” = 1

37.
“Learning Rate” = .1

38.
.0003

39.
Code…

40.
const makeModel = (data, learningRate, iterations) !=> {
let m = 0;
let b = 0;
for (let i = 0; i < iterations; i!++) {
const mGrad =
data.reduce(
(acc, { criticScore, globalSales }) !=>
acc + (m * criticScore + b - globalSales) * criticScore,
0
) / data.length;
const bGrad =
data.reduce(
(acc, { criticScore, globalSales }) !=>
acc + (m * criticScore + b - globalSales),
0
) / data.length;
m -= mGrad * learningRate;
b -= bGrad * learningRate;
}
return { m, b, predict: (criticScore) !=> m * criticScore + b };
};