This document discusses the start of the author's Machine Learning Casual Talks series. It aims to share practical know-how about machine learning applications that is often omitted from academic papers. Topics will include automated testing, key performance indicators, moving beyond just accuracy metrics, and the importance of development data. The author hopes to encourage sharing of real-world experience from their own work at Cookpad.

1. Why I started
Machine Learning
Casual Talks?
Michiaki Ariga (@chezou)
@ DMM.com lab 2015/12/15

2. self
• MLCT / JuliaTokyo / kawasaki.rb
• Software Eng. at Cookpad
• NLPデータマイニングチョットデキル
• TinySegmenter.jl

4. カジュアルとは

5. https://twitter.com/con_mame/status/457130929270435840

6. https://www.tensorflow.org/

7. http://labs.gree.jp/blog/2015/12/14648/
http://d.hatena.ne.jp/sugyan/20151124/1448292129

8. ITエンジニア界隈にも広がる
機械学習熱

9. "Two large YC companies (both with
machine learning teams) have told us that
they consider interest in ML a negative
signal." by Ammon Bartram
https://data.triplebyte.com/who-y-combinator-companies-want-
c1880a08ac88#.kkfadbkbi

10. MLCTの狙い

11. 論文にはあまり出ない
実務で得たノウハウの共有

12. MLCTのテーマ
1. 自動テスト
2. KPI
3. Beyond accuracy
4. DevData

13. DevData

14. Cookpadの場合

15. 俺が、俺達が、DevDataだ！！