カッコをつけきれない私の機械学習 -My machine learning that I can't show off-

D10bbc54b6a5645bcf688d7ea96b02c2?s=47 Kana Kitagawa
February 23, 2019

カッコをつけきれない私の機械学習 -My machine learning that I can't show off-

JAWS DAYS 2019のLT大会でのスライドです。
機械学習できるようになりたい。

This is the slide of JAWS DAYS 2019 LT.
I want to do with machine learning.

D10bbc54b6a5645bcf688d7ea96b02c2?s=128

Kana Kitagawa

February 23, 2019
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Transcript

  1. Χ οί Λ ͭ ͚ ͖ Ε ͳ ͍ ࢲ

    ͷ 
 ػ ց ֶ श M Y M A C H I N E L E A R N I N G T H AT I C A N ’ T S H O W O F F K A N A K I TA G A WA
  2. AT F I R S T…

  3. T H E D E TA I L O F

    T H I S LT C U T S B E C A U S E O F T I M E C O N S T R A I N T S I’ll talk 30% of that I want to talk.
  4. I F Y O U WA N T T O

    
 H E A R M O R E
  5. P L E A S E C O M E

    T O J AW S - U G K O B E .
  6. Welcome to the crazy group @JAWS-UG KOBE Facebook group

  7. A G E N D A •Who am I? •LAST

    YEAR •What did I do for machine learning? •How to use AWS SageMaker •Let’s try
  8. W H O A M I ?

  9. K A N A K I TA G A WA

    • Nickname:Tiger
 #MakikomiTiger • Kansai University student
 3rd grade
 (major:media art) • Internship @ Serverworks • I want to be friend with AWS Lambda and Educate
  10. L A S T Y E A R W H

    AT D I D I D O ?
  11. .BS   JAWS DAYS 2018

  12. A F T E R T H E D AY,

    
 M Y L I F E B E G A N T O C H A N G E .
  13. @JAWS DAYS @re:Invent Taking Photos

  14. I H A D 
 M A C H I

    N E L E A R N I N G T R A I N I N G C L A S S . Because of kind teacher
  15. I WA N T T O S AY “ I

    C A N D O W I T H M A C H I N E L E A R N I N G . ” I think the words cool.
  16. J U S T D O I T ! !

  17. W H AT D I D I D O W

    I T H M A C H I N E L E A R N I N G ?
  18. • Having machine learning training class • Supervised/Unsupervised learning •

    Doing assignment with the book “Machine Learning with Python (O’REILLY)” • Using iris dataset I think it difficult.
  19. W H AT I S 
 I R I S

    D ATA S E T ? •Be distributed in UCI Machine Learning Repository •Iris petal length and width, and calyx length and width •The 3 type(setosa, virginica, versicolor)*50 samples
  20. 3 T Y P E S

  21. O N E T I M E , I WAT

    C H E D T H E S I T E O F T H E D ATA S E T.
  22. T H I S I S P E R H

    A P S T H E B E S T K N O W N D ATA B A S E T O B E F O U N D I N T H E PAT T E R N R E C O G N I T I O N L I T E R AT U R E . https://archive.ics.uci.edu/ml/datasets/iris
  23. I R I S D ATA S E T ?

    I S N ’ T I T B A S I C ? I said I do with Iris dataset
 @SOME JAWS I think it difficult.
  24. T H E B A S I S I S

    I M P O R TA N T. I can’t show off.
  25. M A K E T H E C O U

    R S E C O N T E N T E A S I E R USE Amazon SageMaker
  26. G E N E R A L F L O

    W O F M A C H I N E L E A R N I N G    Make Sample data training of the model deploy the model
  27. A M A Z O N S A G E

    M A K E R • Preprocessing sample data on Jupyter notebook • You can use the algorithm Amazon SageMaker offer. • You can push request to model for inference use boto or high revel Python library • Host model, separate
  28. L E T ’ S T RY.

  29. I H AV E N ’ T U S E

    D A M A Z O N S A G E M A K E R .
  30. AT F I R S T, R E A D

    A N D T RY T U T O R I A L 
 ( U S E M N I S T D ATA S E T ) .
  31. I T R I E D T O D O

    L I K E T H I S T U T O R I A L . B U T I C A N ’ T. Maybe, I can’t understand it well.
  32. I S E A R C H F O R

    
 “ I R I S D ATA S E T 
 A W S S A G E M A K E R ”
  33. @AWS document https://docs.aws.amazon.com/ja_jp/sagemaker/latest/dg/tf- examples.html

  34. T H E B A S I S I S

    I M P O R TA N T. I can’t show off.
  35. J U S T D O I T ! !

    with document
  36. A F T E R M A K E N

    O T E B O O K I N S TA N C E Do Initializing variables
  37. C O N T E N U E https://docs.aws.amazon.com/ja_jp/sagemaker/latest/ dg/tf-example1-train.html

  38. M O D U L E N O T F

    O U N D E R R O R : N O M O D U L E N A M E D ' T E N S O R F L O W ' Error message
  39. I D I D A C C O R D

    I N G T O T H E D O C U M E N T… ?
  40. S A M E P R O B L E

    M S
  41. T H E S O L U T I O

    N
  42. W H E R E W I L L I

    W R I T E … ?
  43. I F O U N D I T.

  44. T E R M I N A L I S

    H E R E ! ! !
  45. Y E S , Y E S ! 
 Y

    O U C A N D O I T ! ! !
  46. T RY A G A I N https://docs.aws.amazon.com/ja_jp/sagemaker/latest/ dg/tf-example1-train.html

  47. Error message AGAIN

  48. None
  49. I T R I E D T O S E

    E O T H E R PA G E .
  50. A C O L L E C T I O

    N O F A M A Z O N S A G E M A K E R S A M P L E N O T E B O O K S .
  51. WAT C H N O T E B O O

    K
  52. Look carefully

  53. I M I S S E D N O T

    E B O O K .
  54. C H A N G E A N D D

    O I T.
  55. I T M O V E S ! ! !

    ! !
  56. M A K E T E N S O R

    F L O W C L A S S I N S TA N C E
  57. G E T T H E I N F O

    R M AT I O N O F T H E T R A I N I N G J O B
  58. D E P L O Y T H E M

    O D E L
  59. AT L A S T…

  60. I N O T I C E D .

  61. I ’ M S O R RY…

  62. I S H O U L D C H E

    C K C A R E F U L LY .
  63. W E ’ R E WA I T I N

    G F O R C H A L L E N G E R .
  64. I’m looking for job . Please talk with me. I

    want to provide a service
 that does not concern
 the country and sex of the person using it.
  65. T H A N K Y O U F O

    R L I S T E N I N G ! ! ! @MakikomiTiger Kana Kitagawa