This is presentation for Natural Language Processing open seminar in Kookmin University. The open seminar reference : https://cafe.naver.com/nlpk My presentation about how to use tensorflow for NLP open seminar for newbies for tensorflow.

3.  We Studied lots of Algorithms in machine Learning, in particular with Deep Learning!

4.  We Studied lots of Algorithms in machine Learning, in particular with Deep Learning!
There are many libraries for those algorithms

5.  There are many libraries and environment for implementing machine learning and deep learning and
Tensorflow is popular and steadily develops.

7.  There are two types of mode in computational operation.

8.  There are two types of mode in computational operation.
Define and Run Define by Run

9.  Tensorflow consists of Node and Edge.

10.  First all, Let’s see example about define and run.

11.  Suppose Tensorflow implements + operation(2 + 3 = 5).
 You can evaluate your model of the Computational Graph by tf.Session.
 The computational graph is evaluated by calling run() method of tf.Session.

12.  Suppose Tensorflow implements add and multiply operation(1+2 and 1*2).

13.  Suppose Tensorflow implements add and multiply operation one more time.

14.  Suppose Tensorflow implements add and multiply operation one more time.

15.  Suppose Tensorflow implements add and multiply operation one more time.

16.  Suppose Tensorflow implements add and multiply operation one more time.

17.  Suppose Tensorflow implements add and multiply operation one more time.

18.  Tensorflow evaluate operation efficiently on execution time.

19.  Tensorflow evaluate operation efficiently on execution time.

20.  Eager execution is comfortable with programming(define by run)

21.  Eager execution is comfortable with programming(define by run)

22.  Eager execution has a benefit that all the functionality of the host language is available while your
models is executing as follows.

23.  During eager execution, you can use tf.GradientTape to trace operations for computing gradient
later.

24.  Traditional Machine Learning and Deep Learning have Learning part.
 But, here we automatically generate data and learn the regression model both normal one and
neural network.

25.  what kind of cost function is when you learn regression model?

26.  How to learn the model on what kind of update function.

27.  What is the linear regression?

28.  The stage of Data generation

29.  Construction of model

30.  Construction of model

31.  Construction of model

32.  Start of learning

33.  The stage of learning

34.  What about sin(x) function for prediction with Neural Network?

35.  The stage of data generation

36.  Hidden layer(1)

37.  Hidden layer(1)

38.  Hidden layer(1)

39.  Hidden layer(1)

40.  Hidden layer(2)

41.  Hidden layer(2)

42.  Hidden layer(2)

43.  (Bonus)Let’s evaluate regression model on eager execution!

44.  (Bonus)Let’s evaluate regression model on eager execution!

45.  (Bonus)Let’s evaluate regression model on eager execution!

46.  (Bonus)Let’s evaluate regression model on eager execution!