Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Python Application Case
Search
Vicky Vernando Dasta
January 27, 2018
Programming
0
65
Python Application Case
Python Workshop @ Kongkow IT 2018
Vicky Vernando Dasta
January 27, 2018
Tweet
Share
More Decks by Vicky Vernando Dasta
See All by Vicky Vernando Dasta
PYTHON 101
vickydasta
0
110
Other Decks in Programming
See All in Programming
Navigating Dependency Injection with Metro
zacsweers
3
2.5k
Namespace and Its Future
tagomoris
6
710
複雑なフォームに立ち向かう Next.js の技術選定
macchiitaka
2
200
パッケージ設計の黒魔術/Kyoto.go#63
lufia
3
440
私の後悔をAWS DMSで解決した話
hiramax
4
210
Putting The Genie in the Bottle - A Crash Course on running LLMs on Android
iurysza
0
140
機能追加とリーダー業務の類似性
rinchoku
2
1.3k
The Past, Present, and Future of Enterprise Java with ASF in the Middle
ivargrimstad
0
160
Zendeskのチケットを Amazon Bedrockで 解析した
ryokosuge
3
310
概念モデル→論理モデルで気をつけていること
sunnyone
3
290
ファインディ株式会社におけるMCP活用とサービス開発
starfish719
0
2k
請來的 AI Agent 同事們在寫程式時,怎麼用 pytest 去除各種幻想與盲點
keitheis
0
120
Featured
See All Featured
Exploring the Power of Turbo Streams & Action Cable | RailsConf2023
kevinliebholz
34
6k
Measuring & Analyzing Core Web Vitals
bluesmoon
9
580
Music & Morning Musume
bryan
46
6.8k
Code Reviewing Like a Champion
maltzj
525
40k
Principles of Awesome APIs and How to Build Them.
keavy
126
17k
JavaScript: Past, Present, and Future - NDC Porto 2020
reverentgeek
52
5.6k
No one is an island. Learnings from fostering a developers community.
thoeni
21
3.4k
Designing Dashboards & Data Visualisations in Web Apps
destraynor
231
53k
How STYLIGHT went responsive
nonsquared
100
5.8k
The Illustrated Children's Guide to Kubernetes
chrisshort
48
50k
Unsuck your backbone
ammeep
671
58k
A Tale of Four Properties
chriscoyier
160
23k
Transcript
PYTHON WORKSHOP HAFIZHAN: WEB APPLICATION WITH PYTHON FLASK VICKY: PYTHON
101 AND APPLICATION IN SECURITY & MACHINE LEARNING
Workshop repo github.com/vickydasta/kongkow-python
About • Student @ physics dept. UR • Research assistant
at photonic lab @ UR • Research interest: photonic, applied machine learning on raspberry pi system • Python user 2014-now
Python at a glance • Multipurpose • OOP (everything is
object) • Dynamic typing • Batteries included • Case sensitive
None
Things we can build computer vision search engine security tools
embedded system web services web application machine learning apps
Your First Python Code print “hello, world!” print(“hello, world!”)
Data Structure • Integer • Float • List • String
• Boolean • Dictionary • Tupple
Data Structure: list • Create an empty list • Add
an item into it • Access item • Remove some item fr = [] fr.append(“guava”) fr[0] fr.remove(“guava”)
Data Structure: tupple • Immutable array • Data are read-only
• Items in tupple can’t be deleted • Can’t add more data once it’s created fixed_data = (1, 2) fixed_data[0] # 1 fixed_data[0] = 1
Data Structure: dict • Named-list • Key-value user = {“name”
“vicky”, “age”: 21} user[“name”] user.keys() user.values() user = {name=“vicky”, age=21}
Control Flow In Python, there are: • if • elif
• else • for • while • continue • break
Control Flow: if if 1 > 0: print “1 larger
than 0”
Control Flow: if-else if 1 > 2: print “hola” else:
print “holi”
Control Flow: if-elif-else if 1 > 2: print “hola” elif
1 > 3: print “holu” else: print “holi”
Loop: for • for loop is for iterating over iterable
object • range function creates list which is iterable • in above case, the i is 0, 1, 2, ..., 99 on each iteration for i in range(100): print(i)
Looping: while • while Requires a condition in order to
start or terminate the loop (while-break) while True: print “hello!” N = 0 while N < 10: N += 1
Function: def • def is the keyword for creating function
def gravity_force(M, m, r): return 6.62e+12*(M*m)/r**2 Function keyword Returned value(s) arguments function name
Coding style in Python • Python uses indentation • 3
spaces or 1 tab for each scope • No brackets or semicolons!
Python Application Case: Machine Learning Security
Machine Learning: Security: Predict land price by its size Port
scanner
Sec: Port scanner • Door knocking analogy • Bruteforce
ML: Linear Regression • Linear model out = mx+b •
Scikit learn search for the m • so the predicted value can be close to y
ML: Datasets X (M2) Y ($ USD) 200 2000 250
3500 300 5000 378 5400 456 6500 680 8700 800 10000
ML: model training datasets model f(x)
ML: prediction new input predicted output Trained Model
ML: scikit-learn • Machine learning is difficult problem • Fortunately,
we have scikit-learn
Python resources • github.com/vinta/awesome-python