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
Gradient Descent Easy
Search
soulchild
July 23, 2014
Science
0
80
Gradient Descent Easy
Easy/brief version of Gradient Descent from Artificial Intelligence Lecture
soulchild
July 23, 2014
Tweet
Share
More Decks by soulchild
See All by soulchild
Similarities between macOS and iOS development
soulchild
0
120
N Tier Architecture for MMORPG
soulchild
0
91
Other Decks in Science
See All in Science
Symfony Console Facelift
chalasr
2
460
MCMCのR-hatは分散分析である
moricup
0
430
生成AIと学ぶPythonデータ分析再入門-Pythonによるクラスタリング・可視化をサクサク実施-
datascientistsociety
PRO
4
1.7k
コンピュータビジョンによるロボットの視覚と判断:宇宙空間での適応と課題
hf149
1
300
Celebrate UTIG: Staff and Student Awards 2025
utig
0
120
データベース11: 正規化(1/2) - 望ましくない関係スキーマ
trycycle
PRO
0
930
LayerXにおける業務の完全自動運転化に向けたAI技術活用事例 / layerx-ai-jsai2025
shimacos
2
1.5k
2025-06-11-ai_belgium
sofievl
1
140
KH Coderチュートリアル(スライド版)
koichih
1
45k
Lean4による汎化誤差評価の形式化
milano0017
1
290
01_篠原弘道_SIPガバニングボード座長_ポスコロSIPへの期待.pdf
sip3ristex
0
640
データベース15: ビッグデータ時代のデータベース
trycycle
PRO
0
330
Featured
See All Featured
Music & Morning Musume
bryan
46
6.7k
What’s in a name? Adding method to the madness
productmarketing
PRO
23
3.6k
How STYLIGHT went responsive
nonsquared
100
5.7k
How to train your dragon (web standard)
notwaldorf
96
6.2k
Practical Tips for Bootstrapping Information Extraction Pipelines
honnibal
PRO
23
1.4k
Scaling GitHub
holman
462
140k
A designer walks into a library…
pauljervisheath
207
24k
Fireside Chat
paigeccino
39
3.6k
Templates, Plugins, & Blocks: Oh My! Creating the theme that thinks of everything
marktimemedia
31
2.5k
Typedesign – Prime Four
hannesfritz
42
2.8k
Connecting the Dots Between Site Speed, User Experience & Your Business [WebExpo 2025]
tammyeverts
8
480
Responsive Adventures: Dirty Tricks From The Dark Corners of Front-End
smashingmag
251
21k
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