Slide Presentasi yang membahas mengenai Tensorflow dan Simple Linear Regression

1. Tech Day
Ngobrol Santai Tensorflow

2. Ramdhan Rizki J @ramdhanrizki
Data Science Researcher
Comrades Developer

4. About Tensorflow
TensorFlow™ is an open source software
library for high performance numerical
computation created by Google.
It comes with strong support for machine
learning and deep learning and the flexible
numerical computation core is used across
many other scientific domains.

5. Installing Tensorflow
# Current release for CPU-only
pip install tensorflow
# Nightly build for CPU-only (unstable)
pip install tf-nightly
# GPU package for CUDA-enabled GPU cards
pip install tensorflow-gpu
# Nightly build with GPU support (unstable)
pip install tf-nightly-gpu
https://www.tensorflow.org/install/

6. Computational Graph

7. Import Tensorflow
import tensorflow as tf

8. Membuat Node Tensor
hello = tf.constant("Hello World", tf.string)
angka = tf.constant(20000, tf.int16)
vokal = tf.constant(["A","I","U","E","O"], tf.string)
squarish_squares = tf.Variable([ [4, 9], [16, 25] ], tf.int32)

9. Menjalankan Session
with tf.Session() as session:
# Pada bagian ini kita bisa menjalankan perintah komputasi graph
hai = session.run(hello)
varAngka = session.run(angka)
varVokal = session.run(vokal)
print(hai)
print(varAngka)
print(varVokal)

10. Constant
anggota = tf.constant(["Ramdhan","Tioreza","Fikri","Satria"])
# Menampilkan shape dari tensor
anggota.get_shape()
# Menampilkan keterangan dari tensor anggota
print(anggota)

11. Variable
# Membuat variable
X = tf.Variable([.5], tf.float32)
# Mengupdate Variable
update = tf.assign(X, [10.5])
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
print(sess.run(X))
sess.run(update)
print(sess.run(X))

12. Placeholder
angka1 = tf.placeholder(tf.float32)
angka2 = tf.placeholder(tf.float32)
tambah = tf.add(angka1, angka2)
with tf.Session() as sess:
print(sess.run([tambah], feed_dict={angka1:[10], angka2:[17]}))

13. Data Type
Data type Python type Description
DT_FLOAT tf.float32 32 bits floating point.
DT_DOUBLE tf.float64 64 bits floating point.
DT_INT8 tf.int8 8 bits signed integer.
DT_INT16 tf.int16 16 bits signed integer.
DT_INT32 tf.int32 32 bits signed integer.
DT_INT64 tf.int64 64 bits signed integer.
DT_UINT8 tf.uint8 8 bits unsigned integer.
DT_STRING tf.string
Variable length byte arrays. Each
element of a Tensor is a byte
array.
DT_BOOL tf.bool Boolean.
DT_COMPLEX64 tf.complex64
Complex number made of two
32 bits floating points: real and
imaginary parts.
DT_COMPLEX128 tf.complex128
Complex number made of two
64 bits floating points: real and
imaginary parts.
DT_QINT8 tf.qint8
8 bits signed integer used in
quantized Ops.
DT_QINT32 tf.qint32
32 bits signed integer used in
quantized Ops.
DT_QUINT8 tf.quint8
8 bits unsigned integer used in
quantized Ops.

14. What is Machine Learning ?

15. Artificial Intelligence
Any technique which enables computers
to mimic human behaviour.
Machine Learning
Subset of AI techniques which use
statistical methods to enable machines
to improve with experiences.
Deep Learning
Subset of ML which make the
computation of multi-layer neural
networks feasible.

16. Tipe Machine Learning
Machine Learning
Supervised Unsupervised Reinforcement

17. Supervised Learning
1. Classification Problem
Ex : Sentiment Analysis, Medical Imaging
2. Regression Problem
Ex : Housing Price Prediction, Bitcoin Price Prediction

18. Unsupervised Learning
1. Clustering
Ex : Customer Segmentation
2. Association
Ex : Market Basket Analysis

19. Simple Linear Regression
Using Tensorflow

20. Simple Linear Regression
Merupakan suatu teknik statistika yang bertujuan untuk
mempelajari hubungan dua variable kontinue serta melakukan
prediksi terhadapnya.

21. X Y
1 1
2 3
3 2
4 6
5 7
6 8
7 10
8 11
9 13
0
2
4
6
8
10
12
14
16
18
20
0 2 4 6 8 10
Y–DEPENDENTVARIABLE
X – INDEPENDENT VARIABLE
SIMPLE LINEAR REGRESSION
How it work

22. X Y
1 1
2 3
3 2
4 6
5 7
6 8
7 10
8 11
9 13
-5
0
5
10
15
20
0 2 4 6 8 10
Y–DEPENDENTVARIABLE
X – INDEPENDENT VARIABLE
SIMPLE LINEAR REGRESSION
How it work

23. -5
0
5
10
15
20
0 2 4 6 8 10
Y–DEPENDENTVARIABLE
X – INDEPENDENT VARIABLE
SIMPLE LINEAR REGRESSION
Persamaan
Y = a*x + b
InterceptSlope
Y = m*x + b

24. Ordinary Least Square

25. X Y (𝒙 − ഥ𝒙) (𝒚 − ഥ𝒚) (𝒙 − ഥ𝒙) (𝒚 − ഥ𝒚) (𝒙 − ഥ𝒙) 𝟐
1 1 -4 -5.78 23,11 16
2 3 -3 -3.78 11,33 9
3 2 -2 -4.78 9,56 4
4 6 -1 -0.78 0,78 1
5 7 0 0.22 0,00 0
6 8 1 1.22 1,22 1
7 10 2 3.22 6,44 4
8 11 3 4.22 12,67 9
9 13 4 6.22 24,89 16
ҧ𝑥 = 5 ത𝑦 = 6.78
෍ = 90 ෍ = 60
Ordinary Least Square

26. Hasil Persamaan
m = 90 / 60
m = 1.5
b = 6.78 – 1.5 * 5 = -0.72
Y = 1.5 * X – 0.72

27. Sum of Squared Error
𝑆𝑆𝐸 = ෍
𝑖=1
𝑛
(𝑦1 − ത𝑦)2

28. X Y Y Predict Error
(𝒚 − ഥ𝒚)
𝒆𝒓𝒓𝒐𝒓 𝟐
1 1 2,22 -1,22 1,49
2 3 3,72 -0,72 0,52
3 2 5,22 -3,22 10,37
4 6 6,72 -0,72 0,52
5 7 8,22 -1,22 1,49
6 8 9,72 -1,72 2,96
7 10 11,22 -1,22 1,49
8 11 12,72 -1,72 2,96
9 13 14,22 -1,22 1,49
ҧ𝑥 = 5 ത𝑦 = 6.78 ത𝑦 = 8.22 𝑆𝑆𝐸 = 23,28
Sum of Squared Error

29. Gradient Descent
Tujuan utama dari metode gradient descent adalah untuk
meminimalisasi cost function.

32. Simple Linear Regression With
Gradient Descent

33. Gradient Descent

34. Lets Build Simple Linear Regression
Using Tensorflow

35. Reference
Andrew Ng Machine Learning Course -
https://www.coursera.org/learn/machine-learning
https://www.tensorflow.org/tutorials/
https://notebooks.azure.com/ramdhanrizki/projects/codelabs-
techday-tensorflow

36. Whats Next

37. http://bit.ly/ml_cheat

38. Books

39. Course
Coursera The Data Science Course
2018: Complete Data
Science Bootcamp

40. Youtube Channel

41. Community
https://stats.stackexchange.com/https://www.floydhub.com/http://kaggle.com/

42. Open Data
https://data.go.id/
https://toolbox.google.com/datasetsearch
https://archive.ics.uci.edu/ml/datasets.html