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
福岡でPyのコミュニティを立ち上げてから約1年のふりかえり
Search
tsurubee
December 12, 2018
Programming
550
1
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
福岡でPyのコミュニティを立ち上げてから約1年のふりかえり
PyFukuokaという福岡のPythonコミュニティの約1年間の活動をふりかえりました
tsurubee
December 12, 2018
More Decks by tsurubee
See All by tsurubee
大規模言語モデルにおけるData-Centric AIと合成データの活用 / Data-Centric AI and Synthetic Data in Large Language Models
tsurubee
1
680
言語モデルによるAI創薬の進展 / Advancements in AI-Driven Drug Discovery Using Language Models
tsurubee
2
670
AIトップカンファレンスからみるData-Centric AIの研究動向 / Research Trends in Data-Centric AI: Insights from Top AI Conferences
tsurubee
3
3.5k
DeepCrysTet: A Deep Learning Approach Using Tetrahedral Mesh for Predicting Properties of Crystalline Materials
tsurubee
0
1.4k
3次元メッシュで表現した結晶構造を用いた材料物性の予測に向けた深層学習モデルの設計 / Design of Deep Learning Model for Predicting Material Properties Using Crystal Structure Represented by Three-Dimensional Mesh
tsurubee
1
2.6k
分散システムの性能異常に対する機械学習の解釈性に基づく原因診断手法 / A Method for Diagnosing the Causes of Performance Issues in Distributed Systems Based on the Interpretability of Machine Learning
tsurubee
0
1.8k
機械学習の解釈性に関する研究動向とシステム運用への応用 / A Survey on Interpretable Machine Learning and Its Application for System Operation
tsurubee
0
400
機械学習モデルの局所的な解釈に着目したシステムにおける異常の原因診断手法の構想
tsurubee
0
8.2k
アニーリングマシンを活用したエッジAIにおける 生成モデルの学習効率化のためのアーキテクチャ
tsurubee
0
1.7k
Other Decks in Programming
See All in Programming
ChromeでローカルLLM
ryu1013
0
140
FactoryBotをやめればRSpecは速くなるのか?
yuta_onishi_97
0
700
AWSに止められる覚悟してますか?
morizo_1984
2
540
AmebaブログはKMPから卒業します
kmars0605
0
200
Performance Optimization With LLMs
xleitix
0
140
ソニーのクラウド共通基盤の変遷とAI時代の開発スタイルに合わせた進化 / The Journey of Sony’s Common Cloud Platform and Its Evolution for AI-Native Development
kenjiyoneyama
0
280
【加筆修正版】Laravel のアプリケーションをどこにデプロイするか #phpcon_ehime
akase244
0
150
[Rails World 2026] Durable orchestration on Rails: from continuation to workflow
palkan
1
750
UnityでSystem.Net.WebSocketsなWebSocketサーバが動かないのでUnity Monoのコードを覗いてみた / about implementing websocket server with unity mono
drumath2237
1
610
作って学ぶSpring BootのAuto Configuration / JJUG Night Seminar 2026-10-19
rshindo
0
180
Jetpack Compose Mechanisms
skydoves
3
820
Everything will be SERVERLESS — 信じて運用した10年の経験値 / Everything Will be Serverless — Lessons Learned from 10 Years of Operational Experience
seike460
PRO
1
1.1k
Featured
See All Featured
DBのスキルで生き残る技術 - AI時代におけるテーブル設計の勘所
soudai
PRO
68
58k
Navigating Weather and Climate Data
rabernat
0
560
RailsConf 2023
tenderlove
30
1.6k
Why You Should Never Use an ORM
jnunemaker
PRO
61
10k
技術選定の審美眼(2025年版) / Understanding the Spiral of Technologies 2025 edition
twada
PRO
120
120k
Large-scale JavaScript Application Architecture
addyosmani
516
110k
We Analyzed 250 Million AI Search Results: Here's What I Found
joshbly
2
2k
30 Presentation Tips
portentint
PRO
1
430
How To Stay Up To Date on Web Technology
chriscoyier
790
250k
Visualizing Your Data: Incorporating Mongo into Loggly Infrastructure
mongodb
50
10k
How STYLIGHT went responsive
nonsquared
100
6.3k
HTML-Aware ERB: The Path to Reactive Rendering @ RubyCon 2026, Rimini, Italy
marcoroth
5
830
Transcript
ԬͰ1ZͷίϛϡχςΟΛ ্ཱ͔ͪ͛ͯΒͷ;Γ͔͑Γ ɹ 'VLVPLB-5
1Z'VLVPLBӡӦऀ ͭΔʔ !UTVSVCFF (.0ϖύϘ ʻܦྺʼ Ώ͔͡Θ !ZVKJLBXB@MFF ΧϥϏφςΫϊϩδʔ
ίϛϡχςΟհ ɾͱͱࣗͨͪͷΞτϓοτͷ͕ཉͯ࢝͘͠Ίͨ ɾֶੜ͞Μ-5͕ॳΊͯͷํ͕ొஃ͢ΔͳͲɺൺֱతΏΔΊͷ ɹΞοτϗʔϜͳίϛϡχςΟͰ͢
͜Ε·Ͱͷ׆ಈ -5Πϕϯτ͘͘ձͳͲ ࡢ݄͔ΒܭΠϕϯτ։࠵
େਓͷࣄͰ࠷ۙΕͯ·ͤΜͰ͕ͨ͠ɺੜ͖ͯ·͢
1Z$PO,ZVTIVͷ্ཱͪ͛ʹࢀՃ 1Z'VLVPLBͷӡӦΛ͍ͬͯͨ͜ͱ͕͖͔͚ͬͱͳΓɺ ࠓͷ݄ʹ1Z$PO,ZVTIVͷ࣮ߦҕһΛ͠·ͨ͠
ࠓޙͷ׆ಈ
དྷ͔Β-5ΠϕϯτΛ࠶࢝ಈ͠·͢ʂ ӡӦऀΛ૿ͯ͠-5ΠϕϯτΛاըதʂ ొஃͯ͘͠ΕΔํେืूʂ ػցֶश͚ͩ͡Όͳ͍1ZUIPO ػցֶशʹڧ͍1ZUIPO
͝ਗ਼ௌ͋Γ͕ͱ͏ ͍͟͝·ͨ͠ʂ