Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Sign up for free
Menu
Search
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Pricing
Search
Sign in
Sign up for free
Gradient Descent Easy
Search
soulchild
July 23, 2014
Science
90
0
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
Gradient Descent Easy
Easy/brief version of Gradient Descent from Artificial Intelligence Lecture
soulchild
July 23, 2014
More Decks by soulchild
See All by soulchild
Similarities between macOS and iOS development
soulchild
0
140
N Tier Architecture for MMORPG
soulchild
0
100
Other Decks in Science
See All in Science
生成AI・プレプリント時代における 研究成果公開の再設計 ― トップカンファレンス文化はどこへ向かうのか / Redesigning the Dissemination of Research Outputs in the Age of Generative AI and Preprints — Where Is the Top-Conference Culture Heading?
ykiyota
0
30k
チュートリアル:世界モデル
hf149
0
2.2k
明治薬科大学講義_ビッグデータ解析を支えるデータベース技術とクラウドコンピューティング
ktatsuya
1
170
Toward Causal Scientific Discovery with AI
sshimizu2006
0
180
2026 Introduction to University Math 01
kanaya
0
140
生成AIが科学とRAにもたらしていること:メタサイエンスの視点から
rmaruy
0
130
ゲームと人工知能
miyayou
0
180
Van Dare naar Durf
voginip
0
300
機械学習 - 決定木からはじめる機械学習
trycycle
PRO
0
1.6k
機械学習 - DBSCAN
trycycle
PRO
0
2.1k
Utiliser Bitcoin sans Internet
rlifchitz
0
370
社内で活躍できるデータサイエンティストになるために
aikinohara
1
160
Featured
See All Featured
The Psychology of Web Performance [Beyond Tellerrand 2023]
tammyeverts
49
3.5k
Exploring the Power of Turbo Streams & Action Cable | RailsConf2023
kevinliebholz
37
6.6k
New Earth Scene 8
popppiees
3
2.5k
Everyday Curiosity
cassininazir
0
310
Ten Tips & Tricks for a 🌱 transition
stuffmc
0
210
Game over? The fight for quality and originality in the time of robots
wayneb77
1
270
GraphQLの誤解/rethinking-graphql
sonatard
75
12k
StorybookのUI Testing Handbookを読んだ
zakiyama
31
6.9k
Paper Plane
katiecoart
PRO
2
53k
Getting science done with accelerated Python computing platforms
jacobtomlinson
2
470
Designing Dashboards & Data Visualisations in Web Apps
destraynor
232
55k
Practical Orchestrator
shlominoach
191
12k
Transcript
Artificial Intelligence Gradient Descent soulchild
Gradient Descent Let computer find the minimum point in a
given graph or equation
Gradient Descent y 0 10 20 30 40 x -6
-4 -2 0 2 4 6 y = x2
Gradient Descent y 0 10 20 30 40 x -6
-4 -2 0 2 4 6 y = x2 Minimum point
Gradient Descent y 0 10 20 30 40 x -6
-4 -2 0 2 4 6 y = x2 Minimum point How does a computer know that this is a minimum point?
Gradient Descent Ans : By brute-forcing the derivative until a
value equal or near to 0 is found y = x2 dy dx = 2x Then guess x by starting from, eg: -6 to 6
Gradient Descent y 0 10 20 30 40 x -6
-4 -2 0 2 4 6 y = x2
Gradient Descent y 0 10 20 30 40 x -6
-4 -2 0 2 4 6 y = x2 dy dx = -12
Gradient Descent y 0 10 20 30 40 x -6
-4 -2 0 2 4 6 y = x2 dy dx = -12 dy dx = —8
Gradient Descent y 0 10 20 30 40 x -6
-4 -2 0 2 4 6 y = x2 dy dx = -12 dy dx = —8 dy dx = -4
Gradient Descent y 0 10 20 30 40 x -6
-4 -2 0 2 4 6 y = x2 dy dx = -12 dy dx = —8 dy dx = -4 dy dx = 0
Gradient Descent y 0 10 20 30 40 x -6
-4 -2 0 2 4 6 y = x2 dy dx = -12 dy dx = —8 dy dx = -4 dy dx = 0 Minimum point found, stop
Gradient Descent A better way to brute force
Gradient Descent y 0 10 20 30 40 x -6
-4 -2 0 2 4 6 y = x2 In short, it works like this
Gradient Descent y 0 10 20 30 40 x -6
-4 -2 0 2 4 6 y = x2 In short, it works like this
Gradient Descent y 0 10 20 30 40 x -6
-4 -2 0 2 4 6 y = x2 In short, it works like this
Gradient Descent y 0 10 20 30 40 x -6
-4 -2 0 2 4 6 y = x2 In short, it works like this
Gradient Descent How to set a good learning rate (α)
?
Gradient Descent When to stop searching ?
Gradient Descent When to stop searching ? Set a maximum
number of iteration
Gradient Descent When to stop searching ? Set a maximum
number of iteration dy dx < n When n can be 0.1, 0.01,.. etc
Gradient Descent Weakness of Gradient Descent 0 3 6 9
12 -8 -6 -4 -2 0 2 4
Gradient Descent Weakness of Gradient Descent 0 3 6 9
12 -8 -6 -4 -2 0 2 4 let say start from here
Gradient Descent Weakness of Gradient Descent 0 3 6 9
12 -8 -6 -4 -2 0 2 4 let say start from here dy dx = 0
Gradient Descent Weakness of Gradient Descent 0 3 6 9
12 -8 -6 -4 -2 0 2 4 let say start from here dy dx = 0 then computer stop finding
Gradient Descent Weakness of Gradient Descent 0 3 6 9
12 -8 -6 -4 -2 0 2 4 let say start from here dy dx = 0 then computer stop finding What about this?! smaller than previous point wor
Gradient Descent Weakness of Gradient Descent Gradient Descent may stuck
in a local minima thus can’t find the global minima
Gradient Descent Q&A