Upgrade to Pro — share decks privately, control downloads, hide ads and more …

A Random Walk in Data Science and Machine Learning in Practice - CEU, Business Analytics Masters - Budapest, Febr 2020

Ce8e94cc306ba164175f693fb01aa8b0?s=47 szilard
February 12, 2020
0
100

A Random Walk in Data Science and Machine Learning in Practice - CEU, Business Analytics Masters - Budapest, Febr 2020

Ce8e94cc306ba164175f693fb01aa8b0?s=128

szilard

February 12, 2020
Tweet

More Decks by szilard

See All by szilard
Ce8e94cc306ba164175f693fb01aa8b0?s=48 szilard
0
31
Ce8e94cc306ba164175f693fb01aa8b0?s=48 szilard
0
38
Ce8e94cc306ba164175f693fb01aa8b0?s=48 szilard
0
17
Ce8e94cc306ba164175f693fb01aa8b0?s=48 szilard
0
18
Ce8e94cc306ba164175f693fb01aa8b0?s=48 szilard
0
31
Ce8e94cc306ba164175f693fb01aa8b0?s=48 szilard
0
76
Ce8e94cc306ba164175f693fb01aa8b0?s=48 szilard
0
22
Ce8e94cc306ba164175f693fb01aa8b0?s=48 szilard
0
34
Ce8e94cc306ba164175f693fb01aa8b0?s=48 szilard
0
7

Featured

See All Featured
433acaea1012b25d97ae66da9b998dad?s=48 malarkey
122
16k
Dad095ea7038f89f760419ce475d5d14?s=48 michaelherold
227
8.9k
48c098b6579220a67a6ba949be6e4b5a?s=48 revolveconf
203
9.9k
E9221b172e78e888355fa2fe4541b595?s=48 mclloyd
PRO
2
7.3k
E14f55d3f939977cecbf51b64ff6f861?s=48 keithpitt
404
20k
3d4519fd34b76fb265fc0237f3792bd4?s=48 danielanewman
6
730
9952dfcd5d338f8a8e7175c8a8f65fb5?s=48 wjessup
342
16k
8081b26e05bb4354f7d65ffc34cbbd67?s=48 chriscoyier
498
130k
A9704266587836f7e784235e5073b93e?s=48 jmmastey
20
5k
282d599b414022eba5096510f675b6e2?s=48 chrislema
174
14k
E837d2996f52008ace055a08fbfff992?s=48 frogandcode
129
20k
Ae14cc4491ac334f9cd23f9f93b4305e?s=48 orderedlist
PRO
331
36k

Transcript

  1. A Random Walk in Data Science and Machine Learning in

    Practice Szilard Pafka, PhD Chief Scientist, Epoch (USA) CEU, Business Analytics Masters Budapest, Febr 2020
  2. None

  3. Disclaimer: I am not representing my employer (Epoch) in this

    talk I cannot confirm nor deny if Epoch is using any of the methods, tools, results etc. mentioned in this talk
  4. None
  5. None

  6. CRISP-DM, 1999

  7. None
  8. None
  9. None
  10. None
  11. None
  12. None
  13. None
  14. None
  15. None
  16. None
  17. None
  18. None
  19. None
  20. None
  21. None
  22. None
  23. None
  24. None
  25. None
  26. None
  27. None
  28. None
  29. None
  30. None
  31. None
  32. None
  33. None
  34. None
  35. None
  36. None
  37. None
  38. None
  39. None
  40. None
  41. None
  42. None
  43. None
  44. None
  45. None
  46. None

  47. Better than Deep Learning: Gradient Boosting Machines (GBM) - 2019

    Updated Edition Szilard Pafka, PhD Chief Scientist, Epoch (USA) Barcelona, Los Angeles, Budapest, Berlin (confs/meetups) 2019
  48. None

  49. Disclaimer: I am not representing my employer (Epoch) in this

    talk I cannot confirm nor deny if Epoch is using any of the methods, tools, results etc. mentioned in this talk

  50. Source: Andrew Ng

  51. Source: Andrew Ng

  52. Source: Andrew Ng

  53. None
  54. None
  55. None
  56. None

  57. Source: https://twitter.com/iamdevloper/

  58. None
  59. None

  60. ...

  61. None
  62. None
  63. None

  64. http://lowrank.net/nikos/pubs/empirical.pdf http://www.cs.cornell.edu/~alexn/papers/empirical.icml06.pdf

  65. http://lowrank.net/nikos/pubs/empirical.pdf http://www.cs.cornell.edu/~alexn/papers/empirical.icml06.pdf

  66. None
  67. None
  68. None
  69. None
  70. None
  71. None
  72. None
  73. None
  74. None
  75. None
  76. None
  77. None
  78. None
  79. None
  80. None
  81. None

  82. Source: Hastie etal, ESL 2ed

  83. Source: Hastie etal, ESL 2ed

  84. Source: Hastie etal, ESL 2ed

  85. Source: Hastie etal, ESL 2ed

  86. None
  87. None
  88. None
  89. None
  90. None
  91. None

  92. 10x

  93. None
  94. None
  95. None

  96. 10x

  97. 10x

  98. None
  99. None
  100. None
  101. None
  102. None
  103. None
  104. None
  105. None
  106. None
  107. None
  108. None
  109. None
  110. None
  111. None
  112. None
  113. None
  114. None
  115. None
  116. None
  117. None
  118. None
  119. None
  120. None
  121. None
  122. None
  123. None
  124. None
  125. None
  126. None
  127. None
  128. None
  129. None
  130. None
  131. None
  132. None
  133. None
  134. None
  135. None
  136. None
  137. None
  138. None

  139. http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf

  140. http://www.argmin.net/2016/06/20/hypertuning/

  141. None
  142. None
  143. None
  144. None
  145. None
  146. None
  147. None

  148. CPU 1

  149. CPU 1 CPU 2

  150. CPU 1 CPU 2

  151. CPU 1 CPU 2

  152. CPU 1 CPU 2

  153. None
  154. None
  155. None
  156. None
  157. None
  158. None
  159. None

  160. *

  161. None

  162. no-one is using this crap

  163. (2018)

  164. (2018)

  165. None

  166. Source: https://www.linkedin.com/pulse/winning-solution-kaggledays-2019-competition-san-francisco-mark-peng/

  167. Source: https://www.linkedin.com/pulse/winning-solution-kaggledays-2019-competition-san-francisco-mark-peng/

  168. Source: https://www.linkedin.com/pulse/winning-solution-kaggledays-2019-competition-san-francisco-mark-peng/

  169. Source: https://www.linkedin.com/pulse/winning-solution-kaggledays-2019-competition-san-francisco-mark-peng/

  170. Source: https://www.linkedin.com/pulse/winning-solution-kaggledays-2019-competition-san-francisco-mark-peng/

  171. Source: https://www.linkedin.com/pulse/winning-solution-kaggledays-2019-competition-san-francisco-mark-peng/

  172. None

  173. More:

  174. None

  175. A Few More Thoughts

  176. None
  177. None
  178. None
  179. None
  180. None
  181. None
  182. None
  183. None
  184. None
  185. None
  186. None
  187. None
  188. None