1. Review
By Artineer

2. Review
Derivative
𝑓=𝑥𝑛
𝜕𝑓 𝜕𝑥 =
𝑛𝑥n-1

3. Review
Derivative
𝑓=𝑎(𝑏 𝑥 )
𝑓=𝑎(𝑏 𝑥 )
𝜕𝑓 𝜕𝑥 =
𝑎 ′ 𝑏 𝑥 × 𝑏′(𝑥)

4. Review
Derivative
𝑓=2𝑥+𝑦
𝜕𝑓 𝜕𝑥 = 2
𝜕𝑓 𝜕𝑦 = 1

5. Review
Hypothesis
𝑦=𝑎𝑥+𝑏
𝑦=𝑤𝑥+𝑏

6. Review
Cost Function
1 2∗10 𝑖=1 10 𝑦𝑖 −(𝑎𝑥𝑖+𝑏) 2 𝑒=

7. Review wn+1 = wn + 𝛼 𝜕𝑒 𝜕𝑤 bn+1 = bn + 𝛼 𝜕𝑒 𝜕𝑏 Gradient Descent
Cost Function
wn+1 = wn + 𝛼 𝜕𝑒 𝜕𝑤
bn+1 = bn + 𝛼 𝜕𝑒 𝜕𝑏

8. Multiple Linear Regression
By Artineer

9. Concept Simple Linear Regression Multiple Linear Regression 𝑥1 𝑥 𝑦 𝑥2𝑥3 𝑥…

10. Concept Simple Linear Regression Multiple Linear Regression 𝑦=𝑤𝑥+𝑏
𝑦=𝑤1𝑥1+𝑤2𝑥2+𝑤3𝑥3+ …+𝑏

11. Concept
Simple
Linear Regression
Multiple
Linear Regression

12. Hypothesis
Y = w1x1 + w2x2 + w3x3 + … + b
It's very complicated.

13. Hypothesis Y = w1x1 + w2x2 + w3x3 + … + b
Y = (x1 x2 x3 …) 𝑤1 𝑤2 𝑤3 … b

14. Hypothesis
Y = (x1 x2 x3 …) 𝑤1 𝑤2 𝑤3 … b
Y = XW + b

15. Cost Function
Simple
Linear Regression
Multiple
Linear Regression

16. 1 2 𝑖=1 10 𝑦𝑖 −(𝑤1𝑥1𝑖+𝑤2𝑥2𝑖+𝑤3𝑥3𝑖+ 𝑏) 2
Cost Function
Simple
Linear Regression
Multiple
Linear Regression
1 2 𝑖=1 10 𝑦𝑖 −(𝑤𝑥𝑖+𝑏) 2
1 2 𝑖=1 10 𝑦𝑖 −(𝑤1𝑥1𝑖+𝑤2𝑥2𝑖+𝑤3𝑥3𝑖+ 𝑏) 2
n = x의 개수

17. e = 1 2 𝑖=1 10 𝑦𝑖 − 𝑤1𝑥1𝑖+𝑤2𝑥2𝑖+𝑤3𝑥3𝑖+𝑏 2
Cost Function
e = 𝑖=1 10 𝑦𝑖 − 𝑤1𝑥1𝑖+𝑤2𝑥2𝑖+𝑤3𝑥3𝑖+𝑏 2

18. Multiple Linear Regression
Gradient Descent
Simple
Linear Regression
Multiple Linear Regression

19. Tensorflow import tensorflow as tf x_data = [1, 2, 3]
y_data = [1, 2, 3]
w = tf.Variable(tf.random_normal([1]), name)

20. Tensorflow import tensorflow as tf
x_data = [1, 2, 3] y_data = [2, 4, 6]
a = tf.Variable(tf.random_normal([1]), name='a') b = tf.Variable(tf.random_normal([1]), name='b')
hypothesis = a * x_train + b
e = tf.reduce_mean(tf.square(hypothesis - y_train))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01) train = optimizer.minimize(e)
sess = tf.Session() sess.run(tf.global_variables_initializer())
for step in range(2001): sess.run(train) if step % 20 == 0: print("iteration : ", step, "e : ", sess.run(e), " ( y = ", sess.run(a),"x + ", sess.run(b), " )")

21. Tensorflow